## Probability and Bayesian modeling [book review]

Posted in Books, Kids, R, Statistics, University life with tags , , , , , , , , , , , , , , , , , on March 26, 2020 by xi'an

Probability and Bayesian modeling is a textbook by Jim Albert [whose reply is included at the end of this entry] and Jingchen Hu that CRC Press sent me for review in CHANCE. (The book is also freely available in bookdown format.) The level of the textbook is definitely most introductory as it dedicates its first half on probability concepts (with no measure theory involved), meaning mostly focusing on counting and finite sample space models. The second half moves to Bayesian inference(s) with a strong reliance on JAGS for the processing of more realistic models. And R vignettes for the simplest cases (where I discovered R commands I ignored, like dplyr::mutate()!).

As a preliminary warning about my biases, I am always reserved at mixing introductions to probability theory and to (Bayesian) statistics in the same book, as I feel they should be separated to avoid confusion. As for instance between histograms and densities, or between (theoretical) expectation and (empirical) mean. I therefore fail to relate to the pace and tone adopted in the book which, in my opinion, seems to dally on overly simple examples [far too often concerned with food or baseball] while skipping over the concepts and background theory. For instance, introducing the concept of subjective probability as early as page 6 is laudable but I doubt it will engage fresh readers when describing it as a measurement of one’s “belief about the truth of an event”, then stressing that “make any kind of measurement, one needs a tool like a scale or ruler”. Overall, I have no particularly focused criticisms on the probability part except for the discrete vs continuous imbalance. (With the Poisson distribution not covered in the Discrete Distributions chapter. And the “bell curve” making a weird and unrigorous appearance there.) Galton’s board (no mention found of quincunx) could have been better exploited towards the physical definition of a prior, following Steve Stiegler’s analysis, by adding a second level. Or turned into an R coding exercise. In the continuous distributions chapter, I would have seen the cdf coming first to the pdf, rather than the opposite. And disliked the notion that a Normal distribution was supported by an histogram of (marathon) running times, i.e. values lower bounded by 122 (at the moment). Or later (in Chapter 8) for Roger Federer’s serving times. Incidentally, a fun typo on p.191, at least fun for LaTeX users, as

$f_{Y\ mid X}$

with an extra space between \’ and mid’! (I also noticed several occurrences of the unvoidable “the the” typo in the last chapters.) The simulation from a bivariate Normal distribution hidden behind a customised R function sim_binom() when it could have been easily described as a two-stage hierarchy. And no comment on the fact that a sample from Y-1.5X could be directly derived from the joint sample. (Too unconscious a statistician?)

When moving to Bayesian inference, a large section is spent on very simple models like estimating a proportion or a mean, covering both discrete and continuous priors. And strongly focusing on conjugate priors despite giving warnings that they do not necessarily reflect prior information or prior belief. With some debatable recommendation for “large” prior variances as weakly informative or (worse) for Exp(1) as a reference prior for sample precision in the linear model (p.415). But also covering Bayesian model checking either via prior predictive (hence Bayes factors) or posterior predictive (with no mention of using the data twice). A very marginalia in introducing a sufficient statistic for the Normal model. In the Normal model checking section, an estimate of the posterior density of the mean is used without (apparent) explanation.

“It is interesting to note the strong negative correlation in these parameters. If one assigned informative independent priors on and , these prior beliefs would be counter to the correlation between the two parameters observed in the data.”

For the same reasons of having to cut on mathematical validation and rigour, Chapter 9 on MCMC is not explaining why MCMC algorithms are converging outside of the finite state space case. The proposal in the algorithmic representation is chosen as a Uniform one, since larger dimension problems are handled by either Gibbs or JAGS. The recommendations about running MCMC do not include how many iterations one “should” run (or other common queries on Stack eXchange), albeit they do include the sensible running multiple chains and comparing simulated predictive samples with the actual data as a  model check. However, the MCMC chapter very quickly and inevitably turns into commented JAGS code. Which I presume would require more from the students than just reading the available code. Like JAGS manual. Chapter 10 is mostly a series of examples of Bayesian hierarchical modeling, with illustrations of the shrinkage effect like the one on the book cover. Chapter 11 covers simple linear regression with some mentions of weakly informative priors,  although in a BUGS spirit of using large [enough?!] variances: “If one has little information about the location of a regression parameter, then the choice of the prior guess is not that important and one chooses a large value for the prior standard deviation . So the regression intercept and slope are each assigned a Normal prior with a mean of 0 and standard deviation equal to the large value of 100.” (p.415). Regardless of the scale of y? Standardisation is covered later in the chapter (with the use of the R function scale()) as part of constructing more informative priors, although this sounds more like data-dependent priors to me in the sense that the scale and location are summarily estimated by empirical means from the data. The above quote also strikes me as potentially confusing to the students, as it does not spell at all how to design a joint distribution on the linear regression coefficients that translate the concentration of these coefficients along y̅=β⁰+β¹x̄. Chapter 12 expands the setting to multiple regression and generalised linear models, mostly consisting of examples. It however suggests using cross-validation for model checking and then advocates DIC (deviance information criterion) as “to approximate a model’s out-of-sample predictive performance” (p.463). If only because it is covered in JAGS, the definition of the criterion being relegated to the last page of the book. Chapter 13 concludes with two case studies, the (often used) Federalist Papers analysis and a baseball career hierarchical model. Which may sound far-reaching considering the modest prerequisites the book started with.

In conclusion of this rambling [lazy Sunday] review, this is not a textbook I would have the opportunity to use in Paris-Dauphine but I can easily conceive its adoption for students with limited maths exposure. As such it offers a decent entry to the use of Bayesian modelling, supported by a specific software (JAGS), and rightly stresses the call to model checking and comparison with pseudo-observations. Provided the course is reinforced with a fair amount of computer labs and projects, the book can indeed achieve to properly introduce students to Bayesian thinking. Hopefully leading them to seek more advanced courses on the topic.

Update: Jim Albert sent me the following precisions after this review got on-line:

[Disclaimer about potential self-plagiarism: this post or an edited version will eventually appear in my Books Review section in CHANCE. As appropriate for a book about Chance!]

## estimating the marginal likelihood (or an information criterion)

Posted in Books, pictures, Statistics, University life with tags , , , , , , , , , , , on December 28, 2019 by xi'an

Tory Imai (from Kyoto University) arXived a paper last summer on what first looked like a novel approximation of the marginal likelihood. Based on the variance of thermodynamic integration. The starting argument is that there exists a power 0<t⁰<1 such that the expectation of the logarithm of the product of the prior by the likelihood to the power t⁰ or t⁰-powered likelihood  is equal to the standard log-marginal

$\log m(x) = \mathbb{E}^{t^0}[ \log f(X|\theta) ]$

when the expectation is under the posterior corresponding to the t⁰-powered likelihood (rather than the full likelihood). By an application of the mean value theorem. Watanabe’s (2013) WBIC replaces the optimum t⁰ with 1/log(n), n being the sample size. The issue in terms of computational statistics is of course that the error of WBIC (against the true log m(x)) is only characterised as an order of n.

The second part of the paper is rather obscure to me, as the motivation for the real log canonical threshold is missing, even though the quantity is connected with the power likelihood. And the DIC effective dimension. It then goes on to propose a new approximation of sBIC, where s stands for singular, of Drton and Plummer (2017) which I had missed (and may ask my colleague Martin later today at Warwick!). Quickly reading through the later however brings explanations about the real log canonical threshold being simply the effective dimension in Schwarwz’s BIC approximation to the log marginal,

$\log m(x) \approx= \log f(x|\hat{\theta}_n) - \lambda \log n +(m-1)\log\log n$

(as derived by Watanabe), where m is called the multiplicity of the real log canonical threshold. Both λ and m being unknown, Drton and Plummer (2017) estimate the above approximation in a Bayesian fashion, which leads to a double indexed marginal approximation for a collection of models. Since this thread leads me further and further from a numerical resolution of the marginal estimation, but brings in a different perspective on mixture Bayesian estimation, I will return to this highly  in a later post. The paper of Imai discusses a different numerical approximation to sBIC, With a potential improvement in computing sBIC. (The paper was proposed as a poster to BayesComp 2020, so I am looking forward discussing it with the author.)

Posted in Books, pictures, Statistics, University life with tags , , , , , , , , , , , , , , , on April 30, 2019 by xi'an

Ziheng Yang and Tianqui Zhu published a paper in PNAS last year that criticises Bayesian posterior probabilities used in the comparison of models under misspecification as “overconfident”. The paper is written from a phylogeneticist point of view, rather than from a statistician’s perspective, as shown by the Editor in charge of the paper [although I thought that, after Steve Fienberg‘s intervention!, a statistician had to be involved in a submission relying on statistics!] a paper , but the analysis is rather problematic, at least seen through my own lenses… With no statistical novelty, apart from looking at the distribution of posterior probabilities in toy examples. The starting argument is that Bayesian model comparison is often reporting posterior probabilities in favour of a particular model that are close or even equal to 1.

“The Bayesian method is widely used to estimate species phylogenies using molecular sequence data. While it has long been noted to produce spuriously high posterior probabilities for trees or clades, the precise reasons for this over confidence are unknown. Here we characterize the behavior of Bayesian model selection when the compared models are misspecified and demonstrate that when the models are nearly equally wrong, the method exhibits unpleasant polarized behaviors,supporting one model with high confidence while rejecting others. This provides an explanation for the empirical observation of spuriously high posterior probabilities in molecular phylogenetics.”

The paper focus on the behaviour of posterior probabilities to strongly support a model against others when the sample size is large enough, “even when” all models are wrong, the argument being apparently that the correct output should be one of equal probability between models, or maybe a uniform distribution of these model probabilities over the probability simplex. Why should it be so?! The construction of the posterior probabilities is based on a meta-model that assumes the generating model to be part of a list of mutually exclusive models. It does not account for cases where “all models are wrong” or cases where “all models are right”. The reported probability is furthermore epistemic, in that it is relative to the measure defined by the prior modelling, not to a promise of a frequentist stabilisation in a ill-defined asymptotia. By which I mean that a 99.3% probability of model M¹ being “true”does not have a universal and objective meaning. (Moderation note: the high polarisation of posterior probabilities was instrumental in our investigation of model choice with ABC tools and in proposing instead error rates in ABC random forests.)

The notion that two models are equally wrong because they are both exactly at the same Kullback-Leibler distance from the generating process (when optimised over the parameter) is such a formal [or cartoonesque] notion that it does not make much sense. There is always one model that is slightly closer and eventually takes over. It is also bizarre that the argument does not account for the complexity of each model and the resulting (Occam’s razor) penalty. Even two models with a single parameter are not necessarily of intrinsic dimension one, as shown by DIC. And thus it is not a surprise if the posterior probability mostly favours one versus the other. In any case, an healthily sceptic approach to Bayesian model choice means looking at the behaviour of the procedure (Bayes factor, posterior probability, posterior predictive, mixture weight, &tc.) under various assumptions (model M¹, M², &tc.) to calibrate the numerical value, rather than taking it at face value. By which I do not mean a frequentist evaluation of this procedure. Actually, it is rather surprising that the authors of the PNAS paper do not jump on the case when the posterior probability of model M¹ say is uniformly distributed, since this would be a perfect setting when the posterior probability is a p-value. (This is also what happens to the bootstrapped version, see the last paragraph of the paper on p.1859, the year Darwin published his Origin of Species.)

## non-local priors for mixtures

Posted in Statistics, University life with tags , , , , , , , , , , , , , , , on September 15, 2016 by xi'an

[For some unknown reason, this commentary on the paper by Jairo Fúquene, Mark Steel, David Rossell —all colleagues at Warwick— on choosing mixture components by non-local priors remained untouched in my draft box…]

Choosing the number of components in a mixture of (e.g., Gaussian) distributions is a hard problem. It may actually be an altogether impossible problem, even when abstaining from moral judgements on mixtures. I do realise that the components can eventually be identified as the number of observations grows to infinity, as demonstrated for instance by Judith Rousseau and Kerrie Mengersen (2011). But for a finite and given number of observations, how much can we trust any conclusion about the number of components?! It seems to me that the criticism about the vacuity of point null hypotheses, namely the logical absurdity of trying to differentiate θ=0 from any other value of θ, applies to the estimation or test on the number of components of a mixture. Doubly so, one might argue, since a very small or a very close component is undistinguishable from a non-existing one. For instance, Definition 2 is correct from a mathematical viewpoint, but it does not spell out the multiple contiguities between k and k’ component mixtures.

The paper starts with a comprehensive coverage of l’état de l’art… When using a Bayes factor to compare a k-component and an h-component mixture, the behaviour of the factor is quite different depending on which model is correct. Essentially overfitted mixtures take much longer to detect than underfitted ones, which makes intuitive sense. And BIC should be corrected for overfitted mixtures by a canonical dimension λ between the true and the (larger) assumed number of parameters  into

2 log m(y) = 2 log p(y|θ) – λ log O(n) + O(log log n)

I would argue that this purely invalidates BIG in mixture settings since the canonical dimension λ is unavailable (and DIC does not provide a useful substitute as we illustrated a decade ago…) The criticism about Rousseau and Mengersen (2011) over-fitted mixture that their approach shrinks less than a model averaging over several numbers of components relates to minimaxity and hence sounds both overly technical and reverting to some frequentist approach to testing. Replacing testing with estimating sounds like the right idea.  And I am also unconvinced that a faster rate of convergence of the posterior probability or of the Bayes factor is a relevant factor when conducting

As for non local priors, the notion seems to rely on a specific topology for the parameter space since a k-component mixture can approach a k’-component mixture (when k'<k) in a continuum of ways (even for a given parameterisation). This topology seems to be summarised by the penalty (distance?) d(θ) in the paper. Is there an intrinsic version of d(θ), given the weird parameter space? Like one derived from the Kullback-Leibler distance between the models? The choice of how zero is approached clearly has an impact on how easily the “null” is detected, the more because of the somewhat discontinuous nature of the parameter space. Incidentally, I find it curious that only the distance between means is penalised… The prior also assumes independence between component parameters and component weights, which I think is suboptimal in dealing with mixtures, maybe suboptimal in a poetic sense!, as we discussed in our reparameterisation paper. I am not sure either than the speed the distance converges to zero (in Theorem 1) helps me to understand whether the mixture has too many components for the data’s own good when I can run a calibration experiment under both assumptions.

While I appreciate the derivation of a closed form non-local prior, I wonder at the importance of the result. Is it because this leads to an easier derivation of the posterior probability? I do not see the connection in Section 3, except maybe that the importance weight indeed involves this normalising constant when considering several k’s in parallel. Is there any convergence issue in the importance sampling solution of (3.1) and (3.3) since the simulations are run under the local posterior? While I appreciate the availability of an EM version for deriving the MAP, a fact I became aware of only recently, is it truly bringing an improvement when compared with picking the MCMC simulation with the highest completed posterior?

The section on prior elicitation is obviously of central interest to me! It however seems to be restricted to the derivation of the scale factor g, in the distance, and of the parameter q in the Dirichlet prior on the weights. While the other parameters suffer from being allocated the conjugate-like priors. I would obviously enjoy seeing how this approach proceeds with our non-informative prior(s). In this regard, the illustration section is nice, but one always wonders at the representative nature of the examples and the possible interpretations of real datasets. For instance, when considering that the Old Faithful is more of an HMM than a mixture.

## model selection and multiple testing

Posted in Books, pictures, Statistics, Travel, University life with tags , , , , , , , , , , , , , on October 23, 2015 by xi'an

Ritabrata Dutta, Malgorzata Bogdan and Jayanta Ghosh recently arXived a survey paper on model selection and multiple testing. Which provides a good opportunity to reflect upon traditional Bayesian approaches to model choice. And potential alternatives. On my way back from Madrid, where I got a bit distracted when flying over the South-West French coast, from Biarritz to Bordeaux. Spotting the lake of Hourtain, where I spent my military training month, 29 years ago!

“On the basis of comparison of AIC and BIC, we suggest tentatively that model selection rules should be used for the purpose for which they were introduced. If they are used for other problems, a fresh justification is desirable. In one case, justification may take the form of a consistency theorem, in the other some sort of oracle inequality. Both may be hard to prove. Then one should have substantial numerical assessment over many different examples.”

The authors quickly replace the Bayes factor with BIC, because it is typically consistent. In the comparison between AIC and BIC they mention the connundrum of defining a prior on a nested model from the prior on the nesting model, a problem that has not been properly solved in my opinion. The above quote with its call to a large simulation study reminded me of the paper by Arnold & Loeppky about running such studies through ecdfs. That I did not see as solving the issue. The authors also discuss DIC and Lasso, without making much of a connection between those, or with the above. And then reach the parametric empirical Bayes approach to model selection exemplified by Ed George’s and Don Foster’s 2000 paper. Which achieves asymptotic optimality for posterior prediction loss (p.9). And which unifies a wide range of model selection approaches.

A second part of the survey considers the large p setting, where BIC is not a good approximation to the Bayes factor (when testing whether or not all mean entries are zero). And recalls that there are priors ensuring consistency for the Bayes factor in this very [restrictive] case. Then, in Section 4, the authors move to what they call “cross-validatory Bayes factors”, also known as partial Bayes factors and pseudo-Bayes factors, where the data is split to (a) make the improper prior proper and (b) run the comparison or test on the remaining data. They also show the surprising result that, provided the fraction of the data used to proper-ise the prior does not converge to one, the X validated Bayes factor remains consistent [for the special case above]. The last part of the paper concentrates on multiple testing but is more tentative and conjecturing about convergence results, centring on the differences between full Bayes and empirical Bayes. Then the plane landed in Paris and I stopped my reading, not feeling differently about the topic than when the plane started from Madrid.