# Statistical Rethinking, by McElreath

Saturday May 8, 2021

I was fortunate to read this excellent book. It's opinionated, often quotable, and a fair summary is that "you can imagine your own generative process, simulate data from it, write the model, and verify that it recovers the true parameter values. You don't have to wait for a mathematician to legalize the model you need." (page 376) Recommended.

"Thinking generatively—how the data could arise—solves many problems. Many statistical problems cannot be solved with statistics. All variables are measured with error. Conditioning on variables creates as many problems as it solves. There is no inference without assumption, but do not choose your assumptions for the sake of inference. Build complex models one piece at a time. Be critical. Be kind." (page 553)

Notes by chapter:

1. The Golem of Prague (statistics, models, science)
2. Small Worlds and Large Worlds (Bayes' theorem)
3. Sampling the Imaginary (priors and posteriors)
4. Geocentric Models (linear regression)
5. The Many Variables & The Spurious Waffles
6. The Haunted DAG & The Causal Terror
7. Ulysses' Compass (overfitting)
8. Conditional Manatees (interactions)
9. Markov Chain Monte Carlo
10. Big Entropy and the Generalized Linear Model
11. God Spiked the Integers (GLMs for counts)
12. Monsters and Mixtures (over-dispersion, ordered categories)
13. Models with Memory (varying effects)
15. Missing Data and Other Opportunities
16. Generalized Linear Madness (beyond GLMs)
17. Horoscopes (conclusion)

### Chapter 1: The Golem of Prague

statistics, models, science

"What researchers need is some unified theory of golem engineering, a set of principles for designing, building, and refining special-purpose statistical procedures. Every major branch of statistical philosophy possesses such a unified theory. But the theory is never taught in introductory—and often not even in advanced—courses. So there are benefits in rethinking statistical inference as a set of strategies, instead of a set of pre-made tools." (page 4)

I think there's a little bit of linguistic confusion between scientific hypotheses ("I think the world works like...") and statistical hypotheses (also called "Statistical models" in Figure 1.2).

"... deductive falsification never works." (page 4)

I think this is too strong.

"modus tollens, which is Latin shorthand for “the method of destruction.”" (page 7)

also: importance of measurement

He has some neat examples of evidence that's not trivial to infer from - whether the ivory-billed woodpecker was extinct, and faster-than-light neutrinos... To these, also add the Piltdown Man fraud.

### Chapter 2: Small Worlds and Large Worlds

Bayes' theorem

Neat counting example in 2.1! I tried to write up a similar example (inspired by Gelman) some years ago...

In note 41, from page 24, McElreath advocates Cox-style probability.

I also wrote up (still years ago) a cute example trying to explain Bayes' rule, but I think it's pretty crummy relative to his development through sections 2.1.2 and 2.1.3.

I kind of miss seeing "evidence" in Bayes' rule... Maybe I like this, with "explanation" for the other term?

``````P(explanation|evidence) = P(evidence|explanation) * P(explanation)
----------------------------------------
P(evidence)``````

(The P's everywhere would kind of obfuscate the nice counting development he was using, but still...)

Then, note that `P(evidence|explanation)` is the "likelihood" of the evidence, and that we're going to talk about that a lot.

Ah, here on page 37 is his version:

``````Posterior = Probability of the data * Prior
-------------------------------
Average probability of the data``````

Also nice! He reminds me that I'm using "evidence" (above) in a different way from using it to mean the denominator there...

There's also the way of doing it that's more like this:

``````Posterior =     Probability of the data     * Prior
-------------------------------
Average probability of the data``````

And then we can talk about the first term as the likelihood ratio, which is kind of nice, but makes it less clear that the denominator is a normalizer that can often be mostly ignored...

Likelihood ratio is a nice thing to think about, especially in connection with Polya's "plausible reasoning"... Evidence that is only consistent with the explanation (and no other) increases confidence a lot.

There's also a nice connection to the error mode of getting the denominator wrong and jumping to conclusions when you don't know of another possible explanation. "I didn't think you were planning a surprise party!" etc.

I don't really like "average probability of the data" as a term, I think...

On page 39, he doesn't include Hamiltonian Monte Carlo as one of the "engines"... Is it a type of MCMC? Ah, yes.

Oh no! Very ugly page break from 42 to 43, with the header of a table separated from its contents...

Interesting; really not explaining what's going on with `dbeta`, conjugate priors, etc... Probably fine?

Wow! I do not understand how this Metropolis algorithm on page 45 works! I guess I can wait until Chapter 9.

Ooh fun, some people have problem solutions online... Here's one:

• https://github.com/cavaunpeu/statistical-rethinking

### Chapter 3: Sampling the Imaginary

priors and posteriors

I'm reading Ellenberg's How Not to Be Wrong, and he says on page 49: "In mathematics, you very seldom get the clearest account of an idea from the person who invented it."

I have that feeling in connection with Gelman and Pearl (not sure they completely invented things they're associated with, but still): I feel like McElreath is doing a better job of explaining things, and it's super nice.

Also Ellenberg:

"If a tiny state like South Dakota experiences a rash of brain cancer, you might presume that the spike is in large measure due to luck, and you might estimate that the rate of brain cancer in the future is likely to be closer to the overall national number. You could accomplish this by taking some kind of weighted average of the South Dakota rate with the national rate. But how to weight the two numbers? That's a bit of an art, involving a fair amount of technical labor I'll spare you here." (pages 70-71)

I think he's referring to multi-level modeling, in the Gelman style.

This common medical testing scenario appeared in a recent LearnedLeague one-day on statistics:

"Suppose that 1% of a population has a particular genetic mutation, and a test for the mutation is 99% accurate for both positive and negative cases. In other words, if someone with the mutation takes the test, there is a 99% chance that the test comes back positive; if someone without the mutation takes the test, there is a 99% chance that the test comes back negative. If a randomly-selected person takes the test and gets a positive result, what is the probability that the person actually has the mutation? (Express your answer as a fraction in lowest terms.)"

I solved it by seeing that 0.01 * 0.99 == 0.99 * 0.01, which is sort of like what McElreath says is called "frequency format" or "natural frequencies." I definitely thought of it in terms of "quantity," but as percentages rather than counts. I was surprised when Erica referred to the problem as "the Bayesian" problem, because I hadn't thought of it that way. So I agree with McElreath that it isn't uniquely Bayesian.

"Changing the representation of a problem often makes it easier to address or inspires new ideas that were not available in an old representation. In physics, switching between Newtonian and Lagrangian mechanics can make problems much easier. In evolutionary biology, switching between inclusive fitness and multilevel selection sheds new light on old models. And in statistics, switching between Bayesian and non-Bayesian representations often teaches us new things about both approaches." (page 50)

"I avoid discussing the analytical approach [of conjugate priors, etc.] in this book, because very few problems are so simple that they have exact analytical solutions like this [the beta-binomial conjugate prior]." (page 560, note for page 51)

The "Why statistics can't save bad science" box on page 51 is neat.

Just to establish equivalence between R and Python...

``````dbinom(6, size=9, prob=0.5)
## [1] 0.1640625``````
``````import scipy.stats
scipy.stats.binom(n=9, p=0.5).pmf(6)
## 0.16406250000000006``````

Interesting: using "compatibility interval" rather than "credible interval" (or "confidence interval") in the sense of "compatible with the model and data." (page 54)

"Overall, if the choice of interval type [percent interval or highest posterior density interval] makes a big difference, then you shouldn't be using intervals to summarize the posterior." (page 58)

"There is no way to really be sure that software works correctly." (page 64)

Hmm; his HPDI (Highest Posterior Density Interval) implementation itself relies on the implementation in coda...

How hard is this really to implement? If you have a histogram or just sorted counts, every left point determines one interval, so you could do it in one pass with a little farting around to find the right point each time, and a running smallest interval... Really not so computation-intensive.

``````birth1 <- c(1,0,0,0,1,1,0,1,0,1,0,0,1,1,0,1,1,0,0,0,1,0,0,0,1,0,
0,0,0,1,1,1,0,1,0,1,1,1,0,1,0,1,1,0,1,0,0,1,1,0,1,0,0,0,0,0,0,0,
1,1,0,1,0,0,1,0,0,0,1,0,0,1,1,1,1,0,1,0,1,1,1,1,1,0,0,1,0,1,1,0,
1,0,1,1,1,0,1,1,1,1)
birth2 <- c(0,1,0,1,0,1,1,1,0,0,1,1,1,1,1,0,0,1,1,1,0,0,1,1,1,0,
1,1,1,0,1,1,1,0,1,0,0,1,1,1,1,0,0,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,
1,1,1,0,1,1,0,1,1,0,1,1,1,0,0,0,0,0,0,1,0,0,0,1,1,0,0,1,0,0,1,1,
0,0,0,1,1,1,0,0,0,0)
table(birth1, birth2)
##        birth2
##  birth1  0  1
##       0 10 39
##       1 30 21``````

So really, what is up with this data?

### Chapter 4:

linear regression

"Linear regression is the geocentric model of applied statistics." (page 71)

Frank's The common patterns of nature seems pretty neat, getting into how common distributions come from processes and information considerations...

"Multiplying small numbers is approximately the same as addition." (page 74)

On page 76 he shows "precision" as τ, meaning 1/σ^2, and it shows up in the equation for the Gaussian with π, which is an example of notation that doesn't work particularly well with the tau manifesto.

"procrustean" (on page 77): "(especially of a framework or system) enforcing uniformity or conformity without regard to natural variation or individuality."

I like the spark histograms!

Oh, neat, they're even called "histosparks"...

And I might have guessed... they're from Hadley.

So there are unicode characters that do blocks of various sizes, by eighths... It looks like Hadley only uses some of them:

``````sparks <- c("\u2581", "\u2582", "\u2583", "\u2585", "\u2587")
#             1/8       2/8       3/8       5/8       7/8``````

Can look these up for example here: https://www.fileformat.info/info/unicode/char/2581/index.htm

" ▁▂▃▄▅▆▇█" has all the heights, with a normal blank at the beginning.

So why does Hadley only use some of the available heights? Not sure.

Oh look at that! In my terminal those all look fine, but in a browser (maybe it depends on font?) the half and full blocks go lower than the others! Still doesn't explain why 6/8 is missing from Hadley's list... Maybe it looks bad in other fonts?

Let's try it fixed-width:

`` ▁▂▃▄▅▆▇█``

Yup, looks much nicer in fixed width.

Here's another nice place to see these: https://en.wikipedia.org/wiki/Block_Elements

"E. T. Jaynes (1922-1988) called this the mind projection fallacy, the mistake of confusing epistemological claims with ontological claims." (page 81)

And a fun reference to Monkeys, Kangaroos, and N:

"... I think you will find that 90% of the past confusions and controversies in statistics have been caused, not by mathematical errors or even ideological differences; but by the technical difficulty that the two parties had different problems in mind, and failed to realize this. Thinking along different lines, each failed to perceive at all what the other considered too obvious to mention." (Jaynes)

"There's also a tradition called dimensionless analysis that advocates constructing variables so that they are unit-less ratios." (page 94)

I haven't heard about this as such, I think. Dimension_al_ analysis is more well known, but not quite the same thing...

Interesting to recall that in the first edition, what's now called `quap` (quadratic approximation posterior / a posteriori?) was called `map` (maximum a posteriori?)

"My experience is that many natural and social scientists have naturally forgotten whatever they once knew about logarithms." (page 98)

"... most social and natural scientists have never had much training in probability theory and tend to get very nervous around ∫'s." (page 106)

He repeats it in different ways here and there, but I noted it again on page 107: I like his effort at clarity between "small world" and "large world" claims, where small world is "assuming the model" or "in the world of the model."

When doing the quadratic example, he z-scores but does not decorrelate... The default behavior in R (using poly) is to "compute orthogonal polynomials"... I'm not sure how common that is elsewhere.

Okay I'll look at sklearn... Here's somebody with a nice Python implementation: http://davmre.github.io/blog/python/2013/12/15/orthogonal_poly But as far as I can tell there isn't anything "built in" for Python...

page 111: `weight.s` is used in one listing, while `weight_s` is used in another, which is a very mild kind of inconsistency. (PR)

"We should feel embarrassed to use [linear models], just so we don't become satisfied with the phenomenological explanations they provide." (page 113)

I really liked section 4.5.2 on splines; I don't think I ever saw a good explanation of splines before.

"Matrix algebra is a stressful topic for many scientists." (page 119)

In both R listings 4.76 and 4.79, it's a little unintuitive to me in that it doesn't seem obvious that `w` is a vector. In ```w ~ dnorm(0, 10)```, that `dnorm` returns just one number. Somewhere `quap` is figuring out how many elements it needs, I guess?

For question 4H8 on page 122, it asks what else would have to change if the intercept was removed from the model. I think the answer is just the priors on the other coefficient(s), since they'd have to get the mean all the way to where it needs to be by themselves then. And/or maybe the data couldn't be centered, because making the mean zero would really hurt the ability to have the result be right? It would still be okay if both x and y were centered, at least for simple designs.

### Chapter 5: The Many Variables & The Spurious Waffles

"... introduce graphical causal models as a way to design and interpret regression models." (page 124)

"About one thing, however, there is general agreement: Causal inference always depends upon unverifiable assumptions." (page 124)

"Think before you regress" (page 128)

In the first paragraph of 5.1.1, I don't really see how Figure 5.2 tells us that only one of the predictor variables has a causal influence...

I really like dagitty. Learning about it is one of the best things in the book, in that I was wishing to find such software while reading The Book of Why but didn't.

It is a little weird that the web interface uses ⊥ (falsum)... Hmm; looking it up, it seems it's the same symbol as `\perp`, which is used for independence. Ah! The "double tack up" (⫫) is for conditional independence! The web interface still uses ⊥ for both kinds of independence.

"This is very weird notation and any feelings of annoyance on your part are justified." (page 130)

The `coeftab` visualization (see page 133) is pretty nice.

It took me a little bit to understand what he was getting at with the "predictor residual plots" (page 135) but I'm glad I did, since it connects to one of his main points about how multiple regression is about how much a variable adds given all the other variables.

"Usually answers to large world questions about truth and causation depend upon information not included in the model." (page 139)

The section 5.2 "Masked relationship" is neat.

"Taking the log of a measure translates the measure into magnitudes." (page 148)

What use of "magnitude" is this? Hmm... Looks like star brightness is done via a log that is called magnitude... Just weird, because in other domains "magnitude" refers to the un-logged value...

Seems like this is less surprising to others, and it makes sense as "order of magnitude."

"A set of DAGs with the same conditional independencies is known as a Markov equivalence set." (page 151)

I was unfamiliar with Melanesia.

One page 155, he makes index variables seem fundamentally different from indicator variables. Their notation in `quap` is different (and nicer) but fundamentally the only difference is with index variables you drop the intercept term (or equivalently, you have separate intercept terms for each thing). Just reading through, I initially thought his index variables were a real novelty, but they're not. (I'm still curious about where he says on page 156 "It is also important to get used to index variables, because multilevel models (Chapter 13) depend upon them.")

"The mistake of accepting the null hypothesis." (page 158)

Question 5E3 on page 159 jokes (I think?) about the effects of amount of funding and size of laboratory on time to PhD, but I'm not sure I know what he thinks is funny...

### Chapter 6: The Haunted DAG & The Causal Terror

On page 161 he starts with Berkson's Paradox, suggesting "selection-distortion effect" as a better name. Dave suggested a nice example: shorter basketball players in the NBA are better 3 point shooters.

"Let's bein with the least of your worries: multicollinearity." (page 163)

This can "smear out" your estimates, because it isn't clear which variables to put beta weight on.

Section 6.2 (page 170) uses "post-treatment bias" to refer to what I might call a mediator, and what he later calls a "pipe" situation (page 185).

"The "d" [in d-separation] stands for directional." (page 174)

"You'll often see the "d" in d-separation defined as "dependency." That would certainly make more sense. But the term d-separation comes from a more general theory of graphs. Directed graphs involve d-separation and undirected graphs involve instead u-separation." (page 563)

"No statistical procedure can substitute for scientific knowledge and attention to it." (page 175)

"If a procedure cannot figure out the truth in a simulated example, we shouldn't trust it in a real one." (page 177)

"So I'm sorry to say that we also have to consider the possibility that our DAG may be haunted." (page 180)

The thing haunting it (as in the title of the chapter) is unmeasured causes that induce collider bias, which means conditioning on things we have can induce bias about effects we're trying to measure.

I like "the four elemental confounds" on page 185. They're pretty similar to the cases I included in What should be in your regression?.

The explanation of shutting the back-door on pages 184-185 is better than others I've seen, I think. And then on page 186 he shows how everybody's favorite dagitty can do it automatically.

Front-door isn't mentioned until page 460.

On page 186, he says "Conditioning on C is the better idea, from the perspective of efficiency, since it could also help with the precision of the estimate of X➔Y." This seems reasonable since C is closer to Y, but I feel like a little more explanation wouldn't have been a bad thing here.

"In fact, domain specific structural causal models can make causal inference possible even when a DAG with the same structure cannot decide how to proceed." (page 188)

Say more? Like, a footnote? An endnote? Some kind of reference to more information? Seems so mysterious!

"Sometimes, in order to avoid multicollinearity, people inspect pairwise correlations among predictors before including them in a model. This is a bad procedure, because what matters it the conditional association, not the association before the variables are included in the model." (page 189)

### Chapter 7: Ulysses' Compass

overfitting

• 7.1. The problem with parameters
• 7.2. Entropy and accuracy
• 7.3. Golem taming: regularization
• 7.4. Predicting predictive accuracy
• 7.5. Model comparison

McElreath has slides and video of his lectures online.

"This chapter describes some of the most commonly used tools for coping with this trade-off." (page 191, referring to the trade-off between simplicity and accuracy)

There's some parallel between statistical models and scientific models generally; see Characteristics of good theories.

"... when we design any particular statistical model, we must decide whether we want to understand causes or rather just predict." (page 192)

"Stargazing" is a cute way to criticize fixation on stars that represent statistical significance. (page 193)

On page 194 he uses "hominin" which I wasn't familiar with. Hominins refers to humans and chimps. Add gorillas and you get hominines. Add orangutans to that and you get hominids.

"In fact, Carl Friedrich Gauss originally derived the OLS procedure in a Bayesian framework." (page 196)

He loves pointing out this kind of thing.

"The point of this example is not to praise R2 but to bury it." (page 197)

This alludes to Shakespeare's famous Marc Antony speech in Julius Caesar: "I come to bury Caesar, not to praise him."

"This means the actual empirical variance, not the variance that R returns with the `var` function, which is a frequentist estimator and therefore has the wrong denominator." (page 197)

Saucy!

"... model fitting can be considered a form of data compression. ... This view of model selection is often known as minimum description length (MDL)." (page 201)

Wikipedia says "In its most basic form, MDL is a model selection principle: the shortest description of the data as the best model."

McElreath points to Grünwald's The Minimum Description Length Principle.

He's trying to develop "out-of-sample deviance" in 7.2 "Entropy and accuracy" starting page 202.

"Likelihood" as in the likelihood of the data, given the model, on page 204. And he really likes it:

"If you see an analysis using something else, either it is a special case of the log scoring rule or it is possibly much worse."

Interesting "Rethinking" box on page 204:

"Calibration is overrated. ... The problem is that calibrated predictions do not have to be good."

It has an endnote on page 563 that includes:

"Strictly speaking, there are no "true" probabilities of events, because probability is epistemological and nature is deterministic."

On page 207 he points out that when probability is zero, L'Hopital's rule gives us 0*log(0) = 0.

Endnote 110 on page 564 begins:

"I really wish I could say there is an accessible introduction to maximum entropy, at the level of most natural and social scientists' math training. If there is, I haven't found it yet."

On page 207 he just says:

"So Bayesian updating is entropy maximization."

He just says "divergence" to mean Kullback-Leibler divergence, and adds in endnote 111 on page 564:

"For what it's worth, Kullback and Leibler make it clear in their 1951 paper that Harold Jeffreys had used this measure already in the development of Bayesian statistics."

There he goes again!

"In plainer language, the divergence is the average difference in log probability between the target (p) and model (q). This divergence is just the difference between two entropies: The entropy of the target distribution p and the cross entropy arising from using q to predict p."

"At this point in the chapter, dear reader, you may be wondering where the chapter is headed." (page 209)

"It's as if we can't tell how far any particular archer is from hitting the target, but we can tell which archer gets closer and by how much." (page 209)

"To compute this [log-probability] score for a Bayesian model, we have to use the entire posterior distribution. Otherwise, vengeful angels will descend upon you." (page 210)

His package has `lppd` for "log-pointwise-predictive-density."

"It is also quite common to see something called the deviance, which is like a lppd score, but multiplied by -2 so that smaller values are better. The 2 is there for historical reasons."

There's more explanation in endnote 112 on page 564:

"In non-Bayesian statistics, under somewhat general conditions, a difference between two deviances has a chi-squared distribution. The factor of 2 is there to scale it the proper way."

(Recall we're interested in the difference between these things; they don't have a meaningful scale on their own.)

I was briefly befuddled by the positive log-likelihoods on page 210, but of course it's the point density, not probability, and the density can be greater than one.

On page 211 he talks about `log_sum_exp` which "takes all the log-probabilities for a given observation, exponentiates each, sums them, then takes the log. But it does this in a way that is numerically stable."

I had cause to do this recently! It comes down to this:

``````import math

def sum_log_prob(a, b):
return max(a, b) + math.log1p(math.exp(0 - abs(a - b)))``````

I based that on a post from Kevin Karplus.

McElreath's is:

``````log_sum_exp <- function( x ) {
xmax <- max(x)
xsum <- sum( exp( x - xmax ) )
xmax + log(xsum)
}``````

(Found on rdrr.)

"That [two-parameter] model does worse in prediction than the model with only 1 parameter, even though the true model does include the additional predictor. This is because with only N=20 cases, the imprecision of the estimate for the first predictor produces more error than just ignoring it." (page 213)

"When you encounter multilevel models in Chapter 13, you'll see that their central device is to learn the strength of the prior from the data itself. So you can think of multilevel models as adaptive regularization, where the model itself tries to learn how skeptical it should be." (page 216)

"Statisticians often make fun of machine learning for reinventing statistics under new names. But regularization is one area where machine learning is more mature. Introductory machine learning courses usually describe regularization. Most introductory statistics courses do not." (page 216)

Section 7.4 (page 217) is "predicting predictive accuracy."

"It is a benign aspect of the universe that this importance [of individual examples] can be estimated without refitting the model." (page 217)

"PSIS" is "Pareto-smoothed importance sampling cross-validation." (page 217)

"For ordinary linear regression with flat priors, the expected overfitting penalty is about twice the number of parameters." (page 219)

"AIC is of mainly historical interest now." (page 219)

It seems like WAIC can only be used when you have a posterior distribution, since it relies on variance of those predictions...

"... in the natural and social sciences the models under consideration are almost never the data-generating models. It makes little sense to attempt to identify a "true" model." (page 221)

"Watanabe recommends computing both WAIC and PSIS and contrasting them. If there are large differences, this implies one or both criteria are unreliable.

"Estimation aside, PSIS has a distinct advantage in warning the user about when it is unreliable." (page 223)

"A very common use of cross-validation and information criteria is to perform model selection, which means choosing the model with the lowest criterion value and then discarding the others. But you should never do this." (page 225)

Endnote 133 references The Decline of Violent Conflicts: What Do The Data Really Say? which is interesting.

"This chapter has been a marathon." (page 235)

And then the chapter summary doesn't even mention cross-validation!

Acronyms:

• AIC: Akaike Information Criterion
• BIC: Bayesian Information Criterion (aka Schwarz criterion)
• CV: Cross-Validation
• DIC: Deviance Information Criterion
• D_{KL}(p, q): Kullback-Leibler divergence
• E: Expectation
• H(p): Entropy
• H(p, q): Cross-entropy
• lppd: Log Pointwise Predictive Density
• MAP: Maximum A posteriori Probability (mode of posterior)
• MDL: Minimum Description Length
• N: sample size
• PSIS: Pareto-smoothed importance sampling cross-validation
• S(q): Sum of log probabilities
• R^2: "variance explained" or "coefficient of determination"
• WAIC: [Widely Applicable | Watanabe-Akaike] Information Criterion

Practice problem 7E1: State the three motivating criteria that define information entropy.

• It should change smoothly with changes in the inputs.
• When more things could happen, it should go up.
• It should add, when you combine things.

Practice problem 7E2: Suppose a coin is weighted such that, when it is tossed and lands on a table, it comes up heads 70% of the time. What is the entropy of this coin?

Well, entropy is the negative sum of p*log(p), so:

``````import math

# Truth (as in Problem 7E1)
p = [0.7, 0.3]

# Entropy, H(p)
H = lambda p: -sum(p_i * math.log(p_i) for p_i in p)
H(p)  # 0.6108643020548935

# Candidate "models"
q = [0.5, 0.5]
r = [0.9, 0.1]

# Cross-Entropy, H(p, q), xH here because Python
xH = lambda p, q: -sum(p_i * math.log(q_i) for p_i, q_i in zip(p, q))
xH(p, q)  # 0.6931471805599453
xH(p, r)  # 0.764527888858692

# KL Divergence, D(p, q)
D = lambda p, q: sum(p_i * math.log(p_i/q_i) for p_i, q_i in zip(p, q))
D(p, q)  # 0.08228287850505178
D(p, r)  # 0.15366358680379852

# D(p, q) = H(p, q) - H(p)
D(p, q) == xH(p, q) - H(p)  # True

# We wish we could do this (use D) but we can't, because we don't have p.

# Data
d = [0, 0, 1]

# Log probability (likelihood) score
S = lambda d, p: sum(math.log(p[d_i]) for d_i in d)
S(d, q)  # -2.0794415416798357
S(d, r)  # -2.513306124309698

# True vs. predictive
S(d, p)  # -1.917322692203401
S(d, [2/3, 1/3])
# -1.9095425048844388

# Deviance
deviance = lambda d, p: -2 * S(d, q)

# Positive log likelihoods! Gasp!

# Note the log probabilities here are really probabilities, because
# I'm just using point estimates, not real distributions. Really,
# you'll have densities, which can be greater than one.``````

"Information criteria construct a theoretical estimate of the relative out-of-sample KL divergence." (page 219)

And he really likes them, largely forgetting about cross-validation.

### Chapter 8: Conditional Manatees

interactions

Propeller marks on manatees are unpleasant, but DID YOU KNOW you see those marks so much because they don't kill the manatees, so they're still around to be seen? Manatees are mostly killed by blunt force thwacking by the hulls of boats, not their propellers.

"Using GDP to measure the health of an economy is like using heat to measure the quality of a chemical reaction." (endnote 138, page 565)

Why not split data to condition on some categorical variable? (page 241)

• For parameters that exist in both parts, "you are essentially making two less-accurate estimates instead of pooling all of the evidence".
• "you can't easily quanitfy that uncertainty" (about "the predictive value of distinguishing" your parts)
• It makes it hard to use information criteria (the comparison works best when the same data is in all the models under comparison)
• Multilevel models don't split the data, and derive benefits in "borrowing information across categories".

On page 245, he explains (again?) that using indicator variables is bad in the sense that it implies more uncertainty in the indicated class (uncertainty of baseline, plus uncertainty of indicator's coefficient).

On using fancy Greek letters in your model specification:

"If your reader cannot say the symbol's name, it could make understanding the model harder." (page 249)

Section 8.2 (page 250) on "Symmetry of interactions" is pretty neat.

"There is just no way to specify a simple, linear interaction in which you can say the effect of some variable x depends upon z but the effect of z does not depend upon x." (page 256)

In endnote 142, McElreath recommends Grafen and Hails' Modern Statistics for the Life Sciences, saying "It has a rather unique geometric presentation of some of the standard linear models." The book has the somewhat surprising subtitle of "Learn to analyse your own data".

Main effects vs. interaction effects.

On weakly informative priors:

"If you displayed these priors to your colleagues, a reasonable summary might be, "These priors contain no bias towards positive or negative effects, and at the same time they very weakly bound the effects to realistic ranges."" (page 260)

"While you can't see them in a DAG, interactions can be important for making accurate inferences." (page 260)

### Chapter 9: Markov Chain Monte Carlo

"Researchers rely upon random numbers for the proper design of experiments." (page 263)

In an endnote, McElreath recommends Kruschke's Doing Bayesian Data Analysis, and it seems like it might be good.

"the combination of parameter values that maximizes posterior probability, the mode, is not actually in a region of parameter values that are highly plausible." (page 269)

"we need MCMC algorithms that focus on the entire posterior at once, instead of one or a few dimensions at a time like Metropolis and Gibbs. Otherwise we get stuck in a narrow, highly curving region of parameter space." (page 270)

"It appears to be a quite general principle that, whenever there is a randomized way of doing something, then there is a nonrandomized way that delivers better performance but requires more thought." (page 270, quoting E. T. Jaynes)

"[The U-turn problem] just shows that the efficiency of HMC comes with the expense of having to tune the leapfrog steps and step size in each application." (page 274)

"Fancy HMC samplers ... choose the leapfrog steps and step size for you ... by conducting a warmup phase in which they try to figure out which step size explores teh posterior efficiently. If you are familiar with older algorithms like Gibbs sampling, which use a burn-in phase, warmup is not like burn-in." (page 274)

### Chapter 10: Big Entropy and the Generalized Linear Model

"Indeed, it may be that no one fully understands [the principle of maximum entropy]." (page 303)

"[The exponential] distribution is the core of survival and event history analysis, which is not covered in this book." (page 315)

"... no regression coefficient ... from a GLM every produces a constant change on the outcome scale. ... every predictor essentially interacts with itself, because the impact of a change in a predictor depends upon the value of the predictor before the change. More generally, every predictor variable effectively interacts with every other predictor variable, whether you explicitly model them as interactions or not." (page 318)

"Link functions are assumptions." (page 319)

He suggests sensitivity assumptions, presumably including trying different link functions, which I think is the closest he comes to talking about probit regression.

"... even a variable that isn't technically a confounder can bias inference, once we have a link function." (page 320)

"Parameter estimates do not by themselves tell you the importancce of a predictor on the outcome." (page 320)

"... a big beta-coefficient may not correspond to a big effect on the outcome." (page 320)

He also points out on page 320 that with a different likelihood (and so link) function, you can't compare log likelihoods (etc.) any more because there's an (unknown) normalization constant that's different between them.

### Chapter 11: God Spiked the Integers

GLMs for counts

1. God spiked the integers
2. 11.1. Binomial regression
• 11.1.1. Logistic regression: Prosocial chimpanzees
• 11.1.2. Relative shark and absolute deer
• 11.1.3. Aggregated binomial: Chimpanzees again, condensed
3. 11.2. Poisson regression
• 11.2.1. Example: Oceanic tool complexity
• 11.2.2. Negative binomial (gamma-Poisson) models
4. 11.3. Multinomial and categorical models
• 11.3.1. Predictors matched to outcomes
• 11.3.2. Predictors matched to observations
• 11.3.3. Multinomial in disguise as Poisson

"As described in Chapter 10, the Poisson model is a special case of binomial." (page 323)

This is a little loose, maybe; Poisson is the limit of binomial, which isn't quite "a special case" I think...

Logistic regression as a special case of binomial regression, okay.

"There are many ways to construct new variables like this, including mutant helper functions." (page 327)

Mutant helper functions? Is this a common term?

"Let's look at these on the outcome scale:" (page 330)

He shows a table that includes logistic regression coefficients, but there's really no attempt to interpret them directly, which is different from some presentations of logistic regression. He goes directly to showing things on the probability scale. He does then show some plots on the coefficient scale, and describes them as being "on the logit scale," but still not a lot of effort spent on connecting them to changes in odds etc.

"counting the rows in the data table is no longer a sensible way to assess sample size." (page 340)

(When using data that is counts of outcomes.)

"This isn't to say that over-parameterizing a model is always a good idea. But it isn't a violation of any statistical principle." (page 345)

"Keep in mind that the number of rows is not clearly the same as the "sample size" in a count model. The relationship between parameters and "degrees of freedom" is not simple, outside of simple linear regressions." (page 347)

"Any rules you've been taught about minimum sample sizes for inference are just non-Bayesian superstitions." (page 347)

He really seems to want to make gamma-Poisson happen (replacing negative binomial).

Probit doesn't appear anywhere! (At least, I haven't seen it and it isn't in the index.)

"In general, more than two things can happen." (page 359)

"The conventional and natural link in this context is the multinomial logit, also known as the softmax function." (page 359)

"Another way to fit a multinomial/categorical model is to refactor it into a series of Poisson likelihoods. That should sound a bit crazy." (page 363)

"It is important to never convert counts to proportions before analysis, because doing so destroys information about sample size." (page 365)

### Chapter 12: Monsters and Mixtures

over-dispersion, ordered categories

"Just be sure to validate it [your model] by simulating dummy data and then recovering the data-generating process through fitting the model to the dummy data." (page 369)

"continuous mixture models in which a linear model is attached not to the observations themselves but rather to a distribution of observations." (page 370)

"... Poisson distributions are very narrow. The variance must equal the mean, recall." (page 373)

"You should not use WAIC and PSIS with these [beta-binomial and gamma-Poisson/negative binomial] models, however, unless you are very sure of what you are doing. The reason is that while ordinary binomial and Poisson models can be aggregated and disaggregated across rows in the data, without changing any causal assumptions, the same is not true of beta-binomial and gamma-Poisson models. The reason is that a beta-binomial or gamma-Poisson likelihood applies an unobserved parameter to each row in the data. When we then go to calculate log-likelihoods, how the data are structured will determine how the beta-distributed or gamma-distributed variation enters the model." (page 375)

"In the sciences, there is sometimes a culture of anxiety surrounding statistical inference. It used to be that researchers couldn't easily construct and study their own custom models, because they had to rely upon statisticians to properly study the models first. This led to concerns about unconventional models, concerns about breaking the laws of statistics. But statistical computing is much more capable now. Now you can imagine your own generative process, simulate data from it, write the model, and verify that it recovers the true parameter values. You don't have to wait for a mathematician to legalize the model you need." (page 376)

This could almost be a summary of the book, maybe.

"Just treating ordered categories as continuous measures is not a good idea."

He offers the cumulative link function.

"This kind of vector, in which all the values sum to one (or any other constant), has a special name, a simplex." (page 394)

### Chapter 13: Models with Memory

It's the varying effects ("random effects") chapter! Multilevel models!

"this prior is actually learned from the data." (pages 399-400)

"When some individuals, locations, or times are sampled more than others, multilevel models automatically cope with differing uncertainty across these clusters. This prevents over-sampled clusters from unfairly dominating inference." (page 400)

This is a pretty cool property to have. The problem of data imbalance is a challenge for many machine learning algorithms. Considering multilevel models as a kind of solution is interesting. Not obvious that it can be easily applied e.g. to vision models, but still, it's interesting.

"When it comes to regression, multilevel regression deserves to be the default approach. There are certainly contexts in which it would be better to use an old-fashioned single-level model. But the contexts in which multilevel models are superior are much more numerous. It is better to begin to build a multilevel analysis, and then realize it's unnecessary, than to overlook it." (page 400)

Is this really the case? It would be neat to see an example where a multilevel model isn't obviously needed but is better.

Costs of multilevel models (page 400, paraphrase):

• new assumptions (priors on priors)
• estimation challenges (requires MCMC)
• hard to understand

Synonyms (page 401):

• multilevel model
• hierarchical model
• mixed effects model

With parameters of multilevel models "most commonly known as random effects". An endnote cites section 6 of Gelman's Anova paper but I didn't find it as "entertaining" as promised. It does include the origin of "varying effects" as a proposed better name than "random effects":

"We define effects (or coefficients) in a multilevel model as constant if they are identical for all groups in a population and varying if they are allowed to differ from group to group." (page 20 in Gelman)

(A "group" could be an individual, depending on the nature of the data.)

I don't love that "hyperparameter" is used for parameters that are learned from the data, even if they're a level up, because it conflicts with the usual ML usage of "hyperparameter". It seems fair that their priors are called hyperpriors, though.

Reasons for using a Gaussian prior (page 403):

1. convention (everybody does it)
2. pragmatism (easy to use/fit)
3. entropy (maxent if specifying only mean and variance)

"Rethinking: Varying intercepts as over-dispersion. ... Compared to a beta-binomial or gamma-Poisson model, a binomial or Poisson model with a varying intercept on every observed outcome will often be easier to estimate and easier to extend." (page 407)

Oh my! A coefficient for every observation! Take that, frequentist statisticians!

It would be interesting to see a direct comparison, using e.g. beta-binomial on the one hand and multilevel on the other...

Page 408 itemizes three perspectives:

• Complete pooling (one estimate shared by all groups)
• No pooling (each group independently)
• Partial pooling (multilevel model; shrinkage of group estimates)

This in particular reminds me of How Not To Sort By Average Rating, which inspired in part my How To Sort By Average Rating advocating Laplace smoothing instead of Wilson bounds.

If you use the grand average to determine the Laplace binomial values, this is just like partial pooling via multilevel model, only much less rigorous, less obviously extensible to multivariate settings, and far easier.

I did a version of Laplace smoothing back when I was helping use survey data to determine how well various medical facilities were satisfying their patients. A ranking was desired, but ranking by raw scores ("no pooling") made the most extreme scores nearly always associated with the locations that had the fewest survey responses.

"Note that the priors are part of the model when we estimate, but not when we simulate. Why? Because priors are epistemology, not ontology. They represent the initial state of information of our robot, not a statement about how nature chooses parameter values." (page 409)

I enjoy that my preferred way of writing the logistic function is used on page 411.

"Partial pooling isn't always better. It's just better on average in the long run." (page 413)

"As soon as you start trusting the machine, the machine will betray your trust." (page 416)

"If the individual units are exhcnagable—the index values could be reassigned without changing the meaning of the model—then partial pooling could help." (page 419)

"Recall that HMC simulates the frictionless flow of a particle on a surface." (page 420)

"Algebra makes many things possible." (page 425)

Ah! Here's where he mentions Mister P: Multilevel Regression and Post-stratification. (page 430)

"Selection on the outcome variable is one of the worst things that can happen in statistics." (page 431)

### Chapter 14: Adventures in Covariance

"... the general varying effects strategy: Any batch of parameters with exchangeable index values can and probably should be pooled. Exchangeable just means the index values have no true ordering, because they are arbitrary labels." (page 435)

"a way to pool information across parameter types—intercepts and slopes" (page 436)

"Finally, we'll circle back to causal inference and use our new powers over covariance to go beyond the tools of Chapter 6 [The Haunted DAG & the Causal Terror], introducing Instrumental Variables." (pages 436-437)

That doesn't reflect the actual order, which has IV in the middle of the chapter...

• 14.1 Varying slopes by construction
• 14.3 Instruments and causal designs
• 14.4 Social relations as correlated varying effects
• 14.5 Continuous categories and the Gaussian Process

"In conventional multilevel models, the device that makes this [modeling the joint population of intercepts and slopes by modeling their covariance] possible is a joint multivariate Gaussian distribution for all of the varying effects, both intercepts and slopes." (page 437)

"... we are always forced to analyze data with a model that is misspecified: The true data-generating process is different than the model." (page 441)

"how you fit the model is part of the model." (page 447)

"This [fewer effective than actual parameters] is a good example of how varying effects adapt to the data. The overfitting risk is much milder here than it would be with ordinary fixed effects." (page 451)

Estimates are pooled/shrunk, so parameters don't fit "tightly" to the data...

"Our interpretation of this experiment has not changed. These chimpanzees simply did not behave in any consistently different way in the partner treatments." (page 452)

This chimpanzee example continues to be fairly dull, for the level of complexity... I guess it's an example of sensitivity analysis, in a sense, looking at it in multiple different ways? But it would be more interesting if there were sometimes different (or any) results.

"There is an obvious cost to these non-centered forms: They look a lot more confusing. Hard-to-read models and model code limit our ability to share implementations with our colleagues, and sharing is the principal goal of scientific computation." (page 454)

"This last line ["Q cannot influence W except through E"] is sometimes called the exclusion restriction. It cannot be strictly tested, and it is often implausible."

The introduction to instrumental variables is based on the classic Does Compulsory School Attendance Affect Schooling and Earnings?

"Remember: With real data, you never know what the right answer is." (page 456)

"Instrumental variables are hard to understand. But there are some excellent tools to help you. For example, the `dagitty` package contains a function `instrumentalVariables` that will find instruments, if they are present in a DAG." (page 459)

"The instrumental variable model is often discussed with an estimation procedure known as two-stage least squares (2SLS). This procedure involves two linear regressions. The predicted values of the first regression are fed into the second as dta, with adjustments so that the standard errors make sense. Amazingly, when the weather is nice, this procedure works. ... Some people mistake 2SLS for the model of instrumental variables. They are not the same thing. Any model can be estimated through a number of different procedures, each with its own benefits and costs." (page 460)

"Instrumental variables are natural experiments that impersonate randomized experiments." (page 460)

Discussing the front-door criterion, he points to a blog post and paper.

• Instrumental Variables
• Front-Door Criterion
• Regression Discontinuity

"First, the correlation changes if we switch the A/B labels." (page 462)

This is a little puzzling. Swapping axes shouldn't change correlation.

Ahhh... It doesn't swap the axes (unless there are only two participants, or an even number that all pair off sufficiently nicely, or the relabeling is otherwise sufficiently "nice"...

Why does this happen...

Some labeling is essentially arbitrary, so that "giver" and "receiver" switch.

Consider a three-point graph. Our "point of view" node is attached to two others. Label them however you want, the give/receive with us remains the same. But when you switch those two, give/receive change direction between them, and if they're not equal, that will send a point over the diagonal and change the correlation.

Cool.

"Social Relations Model, or SRM" (page 462)

"The general approach is known as Gaussian Process regression. This name is unfortunately wholly uninformative about what it is for and how it works." (page 468)

I like the phrase the author uses to describe GP regression: "continuous categories".

"phylogenic, or patristic, distance." (page 481)

"Pagel's lambda" (page 482)

"Biologists tend to use phylogenies under a cloud of superstition and fearful button pushing." (page 482)

"Gaussian processes represent a practical method of extending the varying effects strategy to continuous dimensions of similarity, such as spatial, network, phylogenic, or any other abstract distance between entities in the data." (page 485)

The Stan documenation has more on fitting GP regressions.

I think the thing that keeps this kind of GP from fitting the data perfectly, as is often the case with GPs, is the eta term...

But really, why doesn't it fit the data perfectly? In the primates example, there's a correlation matrix that clearly includes ones...

Oh! It's because the kernel matrix doesn't enter into the mean! ... Well, that's the case for the primates example, anyway...

So the effect of just changing the covariance matrix is like this:

``````install.packages('mvtnorm')
library(mvtnorm)

data <- c(1, 1, -1, -1)

# the mean here defaults to c(0, 0, 0, 0)

# "standard" 4d normal (identity for covariance matrix)
dmvnorm(data)
# 0.003428083

# covariance matrix that expects clustering
sigma <- matrix(c(1, 0.5, 0, 0,
0.5, 1, 0, 0,
0, 0, 1, 0.5,
0, 0, 0.5, 1), nrow=4)
dmvnorm(data, sigma=sigma)
# 0.008902658 (more likely than when assuming independence)``````

So when expecting clustering, you don't have to explain via the mean as much...

For the primates example, he gets a significant coefficient on group size, then he makes it go away via covariance, and then he uses a different covariance and gets it back...

"This [the second] model annihilates group size—the posterior mean is almost zero and there is a lot of mass on both sides of zero. The big change from the previous model suggests that there is a lot of clustering of brain size in the tree and that this produces a spurious relationship with group size, which also clusters in the tree." (page 482)

This is a little weird, isn't it? Just because the relationship clusters in the tree, that doesn't mean there isn't a relationship, right? There are at least two interpretations: (a) bigger groups and bigger brains co-evolved, in this part of the tree, and (b) this part of tree just happens to have both bigger groups and bigger brains. I guess it's a potential confound?

In the final example he gets less covariance and the coefficient on brain size comes back. Which model is more right? Doesn't seem very obvious to me.

Ah: For the earlier example, it's a Poisson regression anyway, so it's not obvious it could fit perfectly anyway, because of the link function.

And the multi-variate normal bit has mean zero! It can only pull out of the mean zero distribution with given covariance (which is constrained by prior so variance isn't very big). So there's really no chance of fitting perfectly.

### Chapter 15: Missing Data and Other Opportunities

• 15.1. "Measurement error" (Oh! Like Nate Silver with polls!)
• 15.2. "Missing data"
• 15.3. "Categorical errors and discrete absences" (sum over options)

"A big advantage of Bayesian inference is that it obviates the need to be clever. ... There's no need to be clever when you can be ruthless." (page 489)

(The ruthlessness is ruthlessness in applying rules of conditional probability.)

"And that's the real trick of the Bayesian approach: to apply conditional probability in all places, for data and parameters." (page 490)

"Bayes is an honest partner. It is not afraid to hurt your feelings." (page 491)

"The big take home point for this section is that when you have a distribution of values, don't reduce it down to a single value to use in a regression." (page 497)

"This [considering covariance between errors] is computationally similar to how we did instrumental variable regression in the previous chapter." (page 498)

It sounds like instrumental variables are often (originally?) about measurement error, but I don't completely understand how...

"Use your background knowledge to write down a generative model or models, simulate data from these models in order to understand the inferential risks, and design a statistical approach that can work at least in theory." (page 499)

"So there will be a posterior distribution for each missing value." (page 505)

In the model, when we have data, the distribution we enter is interpreted as a likelihood, but when we don't have data (it's missing), the distribution is interpreted as a prior... Neat!

"Implementing an imputation model can be done several ways. All of the ways are a little awkward, because the locations of missing values have to be respected, and that means plenty of index management." (page 506)

"Doing better is good." (page 511)

"If you aren't comfortable dropping incomplete cases, then you shouldn't be comfortable using multiple imputation either." (page 511)

This is maybe a little strong; he's explaining here that multiple imputation is an approximation of the technique he's advocating, after all.

He refs this paper, which has some missing data: Complex societies precede moralizing gods throughout world history.

"HMC just doesn't do discrete variables." (page 516)

"This all sounds too good to be true. It is all true. But implementing it is not at all obvious." (page 517)

"This chapter highlights the general principles of the book, that effective statistical modeling requires both careful thought about how the data were generated and delicate attention to numerical algorithms. Neither can lift inference alone." (page 521)

### Chapter 16: Generalized Linear Madness

beyond GLMs

• 16.1. "Geometric people"
• 16.2. "Hidden minds and observed hehavior"
• 16.3. "Ordinary differential nut cracking"
• 16.4. "Population dynamics"

"GLMs (or GLMMs)" (page 526)

"GLMM" is "Generalized linear mixed model" where "mixed" means adding "random effects" in addition to "fixed effects" which means doing something hierarchical, essentially. Varying effects, per individual, group, etc.

"Useful mathematical modeling typically involves ridiculous assumptions." (page 527)

The 1985 "Consider a Spherical Cow: A Course in Environmental Problem Solving" doesn't seem to be the origin of the spherical cow, but it's still fun.

Three cites here:

"One of the major advantages of having a scientifically inspired model is that the parameters have meanings." (page 528)

"The key, as always is to think generatively." (page 531)

Learning curves and teaching when acquiring nut-cracking in humans and chimpanzees

"no lag beyond one period makes any causal sense." (page 543)

I think this is too strong, and he walks it back a little...

"Sometimes all this nonsense is okay, if all you care about is forecasting. But often these models don't even make good forecasts, because getting the future right often depends upon having a decent causal model." (page 543)

This particular model is a famous one, the Lotka-Volterra Model. It models simple predator-prey interactions and demonstrates several important things about ecological dynamics. Lots can be proved about it without using any data at all. For example, the population tends to be unstable, cycling up and down like in Figure 16.6. This is interesting because it suggests that, while nature is more complicated, all that is necessary to see cyclical population dynamics is captured in a stupidly simple model. (page 544)

"The hidden states are the causes. The measurements don't cause anything." (page 549)

### Chapter 17: Horoscopes

conclusion

"Thinking generatively—how the data could arise—solves many problems. Many statistical problems cannot be solved with statistics. All variables are measured with error. Conditioning on variables creates as many problems as it solves. There is no inference without assumption, but do not choose your assumptions for the sake of inference. Build complex models one piece at a time. Be critical. Be kind." (page 553)

"Philosophers of science actually have a term, the pessimistic induction, for the observation that because most science has been wrong, most science is wrong." (page 554)

A Vast Graveyard of Undead Theories: Publication Bias and Psychological Science’s Aversion to the Null

"Even retracted papers continue to be cited." (page 555)

This makes me wonder whether there could be some proactive system to inform authors of such issues... "I see you cited this paper; did you know?"

"The data and its analysis are the scientific product. The paper is just an advertisement." (page 555)