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(This article was first published on <strong><a href="https://xianblog.wordpress.com/2018/04/26/practical-bayesian-inference-book-review/"> R – Xi'an's Og</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p><a rel="nofollow" href="https://amzn.to/2qz6B7R" target="_blank"><img width="444" src="https://i1.wp.com/www.mpia.de/homes/calj/pbi/cover.jpg" height="308"></a><em>[Disclaimer: I received <a rel="nofollow" href="https://amzn.to/2qz6B7R" target="_blank">this book</a> of Coryn Bailer-Jones for a review in the International Statistical Review and intend to submit a revised version of this post as my review. As usual, <a rel="nofollow" href="https://xianblog.wordpress.com/?s=book+review" target="_blank">book reviews</a> on the ‘Og are reflecting my own definitely personal and highly subjective views on the topic!]</em></p>
<p><strong>I</strong>t is always a bit of a challenge to <a rel="nofollow" href="https://xianblog.wordpress.com/?s=book+review" target="_blank">review</a> introductory textbooks as, on the one hand, they are rarely written at the level and with the focus one would personally choose to write them. And, on the other hand, it is all too easy to find issues with the material presented and the way it is presented… So be warned and proceed cautiously! In the current case, <a rel="nofollow" href="https://amzn.to/2qz6B7R" target="_blank">Practical Bayesian Inference</a> tries to embrace too much, methinks, by starting from basic probability notions (that should not be unknown to physical scientists, I believe, and which would avoid introducing a flat measure as a uniform distribution over the real line!, p.20). All the way to running MCMC for parameter estimation, to compare models by Bayesian evidence, and to cover non-parametric regression and bootstrap resampling. For instance, priors only make their apparition on page 71. With a puzzling choice of an improper prior (?) leading to an improper posterior (??), which is certainly not the smoothest entry on the topic. “<em>Improper posteriors are a bad thing</em>“, indeed! And using truncation to turn them into proper distributions is not a clear improvement as the truncation point will significantly impact the inference. Discussing about the choice of priors from the beginning has some appeal, but it may also create confusion in the novice reader (although one never knows!). Even asking about “<em>what is a good prior?</em>” (p.73) is not necessarily the best (and my recommended) approach to a proper understanding of the Bayesian paradigm. And arguing about the unicity of the prior (p.119) clashes with my own view of the prior being primarily a reference measure rather than an ideal summary of the available information. (The <a rel="nofollow" href="https://amzn.to/2qz6B7R" target="_blank">book</a> argues at some point that there is no fixed model parameter, another and connected source of disagreement.) There is a section on assigning priors (p.113), but it only covers the case of a possibly biased coin without much realism. A feature common to many <a rel="nofollow" href="https://xianblog.wordpress.com/2009/01/19/bayesian-choice-of-texbooks/" target="_blank">Bayesian textbooks</a> though. To return to the issue of improper priors (and posteriors), the <a rel="nofollow" href="https://amzn.to/2qz6B7R" target="_blank">book</a> includes several warnings about the danger of hitting an undefined posterior (still called a distribution), without providing real guidance on checking for its definition. (A tough question, to be sure.)</p>
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<p><span><em>“One big drawback of the Metropolis algorithm is that it uses a fixed step size, the magnitude of which can hardly be determined in advance…”(p.165)</em></span></p>
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<p>When introducing computational techniques, quadratic (or Laplace) approximation of the likelihood is mingled with kernel estimators, which does not seem appropriate. Proposing to check convergence and calibrate MCMC via ACF graphs is helpful in low dimensions, but not in larger dimensions. And while warning about the dangers of forgetting the Jacobians in the Metropolis-Hastings acceptance probability when using a transform like η=ln θ is well-taken, the loose handling of changes of variables may be more confusing than helpful (p.167). Discussing and providing <em>two</em> R codes for the (standard) Metropolis algorithm may prove too much. Or not. But using a four page R code for fitting a simple linear regression with a flat prior (pp.182-186) may definitely put the reader off! Even though I deem the example a proper experiment in setting a Metropolis algorithm and appreciate the detailed description around the R code itself. (I just take exception at the paragraph on running the code with two or even one observation, as the fact that “<em>the Bayesian solution always exists</em>” (p.188) [under a proper prior] is not necessarily convincing…)</p>
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<p><span><em>“In the real world we cannot falsify a hypothesis or model any more than we “truthify” it (…) All we can do is ask which of the available models explains the data best.” (p.224)</em></span></p>
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<p>In a similar format, the discussion on testing of hypotheses starts with a lengthy presentation of classical tests and p-values, the chapter ending up with a list of issues. Most of them reasonable in my own referential. I also concur with the conclusive remarks quoted above that what matters is a comparison of (all relatively false) models. What I less agree [as predictable from earlier <a rel="nofollow" href="https://xianblog.wordpress.com/2018/01/17/can-we-have-our-bayesian-cake-and-eat-it-too/" target="_blank">posts</a> and papers] with is the (standard) notion that comparing two models with a Bayes factor follows from the no information (in order to avoid the heavily loaded non-informative) prior weights of ½ and ½. Or similarly that the evidence is uniquely calibrated. Or, again, using a <a rel="nofollow" href="https://xianblog.wordpress.com/2013/11/18/approximation-of-improper-by-vague-priors/" target="_blank">truncated improper prior</a> under one of the assumptions (with the ghost of the <a rel="nofollow" href="https://xianblog.wordpress.com/2013/09/13/lindleys-paradoxes-and-scores/" target="_blank">Jeffreys-Lindley paradox</a> lurking nearby…).  While <a rel="nofollow" href="https://xianblog.wordpress.com/2009/09/24/is-the-dickey-savage-ratio-any-valid/" target="_blank">the Savage-Dickey approximation</a> is mentioned, the first numerical resolution of the approximation to the Bayes factor is via simulations from the priors. Which may be very poor in the situation of vague and uninformative priors. And then the deadly <a rel="nofollow" href="https://xianblog.wordpress.com/2016/05/19/using-mcmc-output-to-efficiently-estimate-bayes-factors/" target="_blank">harmonic mean</a> makes an entry (p.242), along with <a rel="nofollow" href="https://xianblog.wordpress.com/2009/07/13/revised-assmt-of-nested-sampling/" target="_blank">nested sampling</a>… There is also a list of issues about Bayesian model comparison, including (strong) dependence on the prior, <a rel="nofollow" href="https://en.wikipedia.org/wiki/Independence_of_irrelevant_alternatives" target="_blank">dependence on irrelevant alternatives</a>, lack of goodness of fit tests, computational costs, including calls to possibly intractable likelihood function, ABC being then mentioned as a solution (which <a rel="nofollow" href="https://xianblog.wordpress.com/2011/02/24/lack-of-confidence-in-abc-model-choice/" target="_blank">it is not</a>, mostly).</p>
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<h4>Miscellanea</h4>
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<p><span><em>“This problem is known as the curse of dimensionality (an unusually evocative name for the field of statistics).”(p.66)</em></span></p>
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<p>Starting an inference book with the (in)famous <a rel="nofollow" href="https://xianblog.wordpress.com/2012/11/23/paradoxes-in-scientific-inference/" target="_blank">Monty Hall paradox</a> is maybe not the most helpful entry to Bayesian inference (since some of my Bayesian friends managed to fail solving the paradox!). Some notations may feel more natural for physicists than mathematicians, as for instance the loose handling of changes of variables, e.g. P(ln x) representing the density of the log transform Y=ln X… Or {θ} denoting a sample of θ’s. Introducing (unprincipled) estimators like the unbiased estimator of the variance out of the blue is somewhat contradictory with a Bayesian perspective, which produces estimators from a decisional perspective (and none of them unbiased). Using the same symbol for the (unknown) parameter and its estimate (see, e.g., p.77 for the simple linear model) does not help. It is however interesting to notice how the author insists on parameterisation and normalisation issues, but I cannot help pointing the <a rel="nofollow" href="https://amzn.to/2qz6B7R" target="_blank">book</a> missing the dominating measure aspect of deriving a <a rel="nofollow" href="https://xianblog.wordpress.com/2009/09/12/map-estimators-are-not-truly-bayesian-estimators/" target="_blank">MAP estimate</a>. Pages and pages of printed R output are not necessarily very helpful, considering the codes are available on-line. A section (p.140) on how to handle underflows resulting from small likelihoods and large samples is more helpful. Another curious occurrence is the rather long discussion on the p-value, in the middle (p.111) of the chapter on parameter estimation. When considering the Jeffreys prior, invariance under reparameterisation is advanced as a supportive argument, as in my books, but the multidimensional case still remains a mystery. Yet another <a rel="nofollow" href="https://xianblog.wordpress.com/2009/05/25/add-on-to-my-favourites/" target="_blank">curious instance</a> of the fact that densities are expressed in <em>units</em> that are the inverses of the variate. Volume units?</p>
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