**I**n the past weeks I have received and read several papers (and X validated entries)where the Bayes factor is used to compare priors. Which does not look right to me, not on the basis of my general dislike of Bayes factors!, but simply because this seems to clash with the (my?) concept of Bayesian model choice and also because data should not play a role in that situation, from being used to select a *prior*, hence at least twice to run the inference, to resort to a *single* parameter value (namely the one behind the data) to decide between two distributions, to having no asymptotic justification, to eventually favouring the prior concentrated on the maximum likelihood estimator. And more. But I fear that this reticence to test for prior adequacy also extends to the prior predictive, or Box’s p-value, namely the probability under this prior predictive to observe something “more extreme” than the current observation, to quote from David Spiegelhalter.

## Archive for The Bayesian Choice

## leave Bayes factors where they once belonged

Posted in Statistics with tags Bayes factors, Bayesian Analysis, Bayesian decision theory, cross validated, prior comparison, prior predictive, prior selection, The Bayesian Choice, The Beatles, using the data twice, xkcd on February 19, 2019 by xi'an## a glaring mistake

Posted in Statistics with tags Bayes factor, Bayesian hypothesis testing, Bayesian textbook, cross validated, Stack Exchange, The Bayesian Choice, typos on November 28, 2018 by xi'an**S**omeone posted this question about Bayes factors in my book on Saturday morning and I could not believe the glaring typo pointed out there had gone through the centuries without anyone noticing! There should be no index 0 or 1 on the θ’s in either integral (or indices all over). I presume I made this typo when cutting & pasting from the previous formula (which addressed the case of two point null hypotheses), but I am quite chagrined that I sabotaged the definition of the Bayes factor for generations of readers of the Bayesian Choice. Apologies!!!

## back to the Bayesian Choice

Posted in Books, Kids, Statistics, University life with tags autoregressive model, Bayesian decision theory, Book, exercises, improper posteriors, improper prior, inverse Gamma distribution, prior predictive, The Bayesian Choice on October 17, 2018 by xi'an**S**urprisingly (or not?!), I received two requests about some exercises from The Bayesian Choice, one from a group of students from McGill having difficulties solving the above, wondering about the properness of the posterior (but missing the integration of x), to whom I sent back this correction. And another one from the Czech Republic about a difficulty with the term “evaluation” by which I meant (pardon my French!) estimation.

## about paradoxes

Posted in Books, Kids, Statistics, University life with tags bias, book review, email, Jacobian, Mark Chang, MLE, paradoxes, reparameterisation, scientific inference, The Bayesian Choice, unbiasedness on December 5, 2017 by xi'an**A**n email I received earlier today about statistical paradoxes:

I am a PhD student in biostatistics, and an avid reader of your work. I recently came across this blog post, where you review a text on statistical paradoxes, and I was struck by this section:

I found this section provocative, but I am unclear on the nature of these “paradoxes”. I reviewed my stat inference notes and came across the classic example that there is no unbiased estimator for 1/p w.r.t. a binomial distribution, but I believe you are getting at a much more general result. If it’s not too much trouble, I would sincerely appreciate it if you could point me in the direction of a reference or provide a bit more detail for these two “paradoxes”.

The text is Chang’s Paradoxes in Scientific Inference, which I indeed reviewed negatively. To answer about the bias “paradox”, it is indeed a neglected fact that, while the average of *any* transform of a sample obviously is an unbiased estimator of its mean (!), the converse does not hold, namely, an *arbitrary* transform of the model parameter θ is not necessarily enjoying an unbiased estimator. In Lehmann and Casella, Chapter 2, Section 4, this issue is (just slightly) discussed. But essentially, transforms that lead to unbiased estimators are mostly the polynomial transforms of the mean parameters… (This also somewhat connects to a recent X validated question as to why MLEs are not always unbiased. Although the simplest explanation is that the transform of the MLE is the MLE of the transform!) In exponential families, I would deem the range of transforms with unbiased estimators closely related to the collection of functions that allow for inverse Laplace transforms, although I cannot quote a specific result on this hunch.

The other “paradox” is that, if h(X) is the MLE of the model parameter θ for the observable X, the distribution of h(X) has a density different from the density of X and, hence, its maximisation in the parameter θ may differ. An example (my favourite!) is the MLE of ||a||² based on x N(a,I) which is ||x||², a poor estimate, and which (strongly) differs from the MLE of ||a||² based on ||x||², which is close to (1-p/||x||²)²||x||² and (nearly) admissible [as discussed in the Bayesian Choice].

## relativity is the keyword

Posted in Books, Statistics, University life with tags Bayes factor, model posterior probabilities, OxWaSP, relativity, Saint Giles cemetery, testing of hypotheses, The Bayesian Choice, University of Oxford on February 1, 2017 by xi'an**A**s I was teaching my introduction to Bayesian Statistics this morning, ending up with the chapter on tests of hypotheses, I found reflecting [out loud] on the relative nature of posterior quantities. Just like when I introduced the role of priors in Bayesian analysis the day before, I stressed the relativity of quantities coming out of the BBB [Big Bayesian Black Box], namely that whatever happens as a Bayesian procedure is to be understood, scaled, and relativised against the prior equivalent, i.e., that the reference measure or gauge is the prior. This is sort of obvious, clearly, but bringing the argument forward from the start avoids all sorts of misunderstanding and disagreement, in that it excludes the claims of absolute and certainty that may come with the production of a posterior distribution. It also removes the endless debate about the determination of *the* prior, by making *each* prior a reference on its own. With an additional possibility of calibration by simulation under the assumed model. Or an alternative. Again nothing new there, but I got rather excited by this presentation choice, as it seems to clarify the path to Bayesian modelling and avoid misapprehensions.

Further, the curious case of the Bayes factor (or of the posterior probability) could possibly be resolved most satisfactorily in this framework, as the [dreaded] dependence on the model prior probabilities then becomes a matter of relativity! Those posterior probabilities depend directly and almost linearly on the prior probabilities, but they should not be interpreted in an *absolute* sense as the ultimate and unique probability of the hypothesis (which anyway does not mean anything in terms of the observed experiment). In other words, this posterior probability does not need to be scaled against a U(0,1) distribution. Or against the *p*-value if anyone wishes to do so. By the end of the lecture, I was even wondering [not so loudly] whether or not this perspective was allowing for a resolution of the Lindley-Jeffreys paradox, as the resulting number could be set relative to the choice of the [arbitrary] normalising constant. Continue reading

## same data – different models – different answers

Posted in Books, Kids, Statistics, University life with tags Beaumont-en-Auge, Jeffreys priors, Laplace succession rule, non-informative priors, Normandie, Pierre Simon de Laplace, reference priors, statue, The Bayesian Choice, wikipedia on June 1, 2016 by xi'an**A**n interesting question from a reader of the Bayesian Choice came out on X validated last week. It was about Laplace’s succession rule, which I found somewhat over-used, but it was nonetheless interesting because the question was about the discrepancy of the “non-informative” answers derived from two models applied to the data: an Hypergeometric distribution in the Bayesian Choice and a Binomial on Wikipedia. The originator of the question had trouble with the difference between those two “non-informative” answers as she or he believed that there was a *single* non-informative principle that should lead to a unique answer. This does not hold, even when following a reference prior principle like Jeffreys’ invariant rule or Jaynes’ maximum entropy tenets. For instance, the Jeffreys priors associated with a Binomial and a Negative Binomial distributions differ. And even less when considering that there is no unity in reaching those reference priors. (Not even mentioning the issue of the reference dominating measure for the definition of the entropy.) This led to an informative debate, which is the point of X validated.

On a completely unrelated topic, the survey ship looking for the black boxes of the crashed EgyptAir plane is called the Laplace.