Archive for Jeffreys priors

O’Bayes in action

Posted in Books, Kids, Statistics, University life with tags , , , , , , , , , , , , on November 7, 2017 by xi'an

My next-door colleague [at Dauphine] François Simenhaus shared a paradox [to be developed in an incoming test!] with Julien Stoehr and I last week, namely that, when selecting the largest number between a [observed] and b [unobserved], drawing a random boundary on a [meaning that a is chosen iff a is larger than this boundary] increases the probability to pick the largest number above ½2…

When thinking about it in the wretched RER train [train that got immobilised for at least two hours just a few minutes after I went through!, good luck to the passengers travelling to the airport…] to De Gaulle airport, I lost the argument: if a<b, the probability [for this random bound] to be larger than a and hence for selecting b is 1-Φ(a), while, if a>b, the probability [of winning] is Φ(a). Hence the only case when the probability is ½ is when a is the median of this random variable. But, when discussing the issue further with Julien, I exposed an interesting non-informative prior characterisation. Namely, if I assume a,b to be iid U(0,M) and set an improper prior 1/M on M, the conditional probability that b>a given a is ½. Furthermore, the posterior probability to pick the right [largest] number with François’s randomised rule is also ½, no matter what the distribution of the random boundary is. Now, the most surprising feature of this coffee room derivation is that these properties only hold for the prior 1/M. Any other power of M will induce an asymmetry between a and b. (The same properties hold when a,b are iid Exp(M).)  Of course, this is not absolutely unexpected since 1/M is the invariant prior and since the “intuitive” symmetry only holds under this prior. Power to O’Bayes!

When discussing again the matter with François yesterday, I realised I had changed his wording of the puzzle. The original setting is one with two cards hiding the unknown numbers a and b and of a player picking one of the cards. If the player picks a card at random, there is indeed a probability of ½ of picking the largest number. If the decision to switch or not depends on an independent random draw being larger or smaller than the number on the observed card, the probability to get max(a,b) in the end hits 1 when this random draw falls into (a,b) and remains ½ outside (a,b). Randomisation pays.

Jeffreys priors for mixtures [or not]

Posted in Books, Statistics, University life with tags , , , , , on July 25, 2017 by xi'an

Clara Grazian and I have just arXived [and submitted] a paper on the properties of Jeffreys priors for mixtures of distributions. (An earlier version had not been deemed of sufficient interest by Bayesian Analysis.) In this paper, we consider the formal Jeffreys prior for a mixture of Gaussian distributions and examine whether or not it leads to a proper posterior with a sufficient number of observations.  In general, it does not and hence cannot be used as a reference prior. While this is a negative result (and this is why Bayesian Analysis did not deem it of sufficient importance), I find it definitely relevant because it shows that the default reference prior [in the sense that the Jeffreys prior is the primary choice in nonparametric settings] does not operate in this wide class of distributions. What is surprising is that the use of a Jeffreys-like prior on a global location-scale parameter (as in our 1996 paper with Kerrie Mengersen or our recent work with Kaniav Kamary and Kate Lee) remains legit if proper priors are used on all the other parameters. (This may be yet another illustration of the tequilla-like toxicity of mixtures!)

Francisco Rubio and Mark Steel already exhibited this difficulty of the Jeffreys prior for mixtures of densities with disjoint supports [which reveals the mixture latent variable and hence turns the problem into something different]. Which relates to another point of interest in the paper, derived from a 1988 [Valencià Conference!] paper by José Bernardo and Javier Giròn, where they show the posterior associated with a Jeffreys prior on a mixture is proper when (a) only estimating the weights p and (b) using densities with disjoint supports. José and Javier use in this paper an astounding argument that I had not seen before and which took me a while to ingest and accept. Namely, the Jeffreys prior on a observed model with latent variables is bounded from above by the Jeffreys prior on the corresponding completed model. Hence if the later leads to a proper posterior for the observed data, so does the former. Very smooth, indeed!!!

Actually, we still support the use of the Jeffreys prior but only for the mixture mixtures, because it has the property supported by Judith and Kerrie of a conservative prior about the number of components. Obviously, we cannot advocate its use over all the parameters of the mixture since it then leads to an improper posterior.

same data – different models – different answers

Posted in Books, Kids, Statistics, University life with tags , , , , , , , , , on June 1, 2016 by xi'an

An 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.

covariant priors, Jeffreys and paradoxes

Posted in Books, Statistics, University life with tags , , , , , , , , , , , on February 9, 2016 by xi'an

“If no information is available, π(α|M) must not deliver information about α.”

In a recent arXival apparently submitted to Bayesian Analysis, Giovanni Mana and Carlo Palmisano discuss of the choice of priors in metrology. Which reminded me of this meeting I attended at the Bureau des Poids et Mesures in Sèvres where similar debates took place, albeit being led by ferocious anti-Bayesians! Their reference prior appears to be the Jeffreys prior, because of its reparameterisation invariance.

“The relevance of the Jeffreys rule in metrology and in expressing uncertainties in measurements resides in the metric invariance.”

This, along with a second order approximation to the Kullback-Leibler divergence, is indeed one reason for advocating the use of a Jeffreys prior. I at first found it surprising that the (usually improper) prior is used in a marginal likelihood, as it cannot be normalised. A source of much debate [and of our alternative proposal].

“To make a meaningful posterior distribution and uncertainty assessment, the prior density must be covariant; that is, the prior distributions of different parameterizations must be obtained by transformations of variables. Furthermore, it is necessary that the prior densities are proper.”

The above quote is quite interesting both in that the notion of covariant is used rather than invariant or equivariant. And in that properness is indicated as a requirement. (Even more surprising is the noun associated with covariant, since it clashes with the usual notion of covariance!) They conclude that the marginal associated with an improper prior is null because the normalising constant of the prior is infinite.

“…the posterior probability of a selected model must not be null; therefore, improper priors are not allowed.”

Maybe not so surprisingly given this stance on improper priors, the authors cover a collection of “paradoxes” in their final and longest section: most of which makes little sense to me. First, they point out that the reference priors of Berger, Bernardo and Sun (2015) are not invariant, but this should not come as a surprise given that they focus on parameters of interest versus nuisance parameters. The second issue pointed out by the authors is that under Jeffreys’ prior, the posterior distribution of a given normal mean for n observations is a t with n degrees of freedom while it is a t with n-1 degrees of freedom from a frequentist perspective. This is not such a paradox since both distributions work in different spaces. Further, unless I am confused, this is one of the marginalisation paradoxes, which more straightforward explanation is that marginalisation is not meaningful for improper priors. A third paradox relates to a contingency table with a large number of cells, in that the posterior mean of a cell probability goes as the number of cells goes to infinity. (In this case, Jeffreys’ prior is proper.) Again not much of a bummer, there is simply not enough information in the data when faced with a infinite number of parameters. Paradox #4 is the Stein paradox, when estimating the squared norm of a normal mean. Jeffreys’ prior then leads to a constant bias that increases with the dimension of the vector. Definitely a bad point for Jeffreys’ prior, except that there is no Bayes estimator in such a case, the Bayes risk being infinite. Using a renormalised loss function solves the issue, rather than introducing as in the paper uniform priors on intervals, which require hyperpriors without being particularly compelling. The fifth paradox is the Neyman-Scott problem, with again the Jeffreys prior the culprit since the estimator of the variance is inconsistent. By a multiplicative factor of 2. Another stone in Jeffreys’ garden [of forking paths!]. The authors consider that the prior gives zero weight to any interval not containing zero, as if it was a proper probability distribution. And “solve” the problem by avoid zero altogether, which requires of course to specify a lower bound on the variance. And then introducing another (improper) Jeffreys prior on that bound… The last and final paradox mentioned in this paper is one of the marginalisation paradoxes, with a bizarre explanation that since the mean and variance μ and σ are not independent a posteriori, “the information delivered by x̄ should not be neglected”.

uniform correlation mixtures

Posted in Books, pictures, Statistics, University life with tags , , , , , , , , , on December 4, 2015 by xi'an

Philadelphia, Nov. 01, 2010Kai Zhang and my friends from Wharton, Larry Brown, Ed George and Linda Zhao arXived last week a neat mathematical foray into the properties of a marginal bivariate Gaussian density once the correlation ρ is integrated out. While the univariate marginals remain Gaussian (unsurprising, since these marginals do not depend on ρ in the first place), the joint density has the surprising property of being

[1-Φ(max{|x|,|y|})]/2

which turns an infinitely regular density into a density that is not even differentiable everywhere. And which is constant on squares rather than circles or ellipses. This is somewhat paradoxical in that the intuition (at least my intuition!) is that integration increases regularity… I also like the characterisation of the distributions factorising through the infinite norm as scale mixtures of the infinite norm equivalent of normal distributions. The paper proposes several threads for some extensions of this most surprising result. Other come to mind:

  1. What happens when the Jeffreys prior is used in place of the uniform? Or Haldane‘s prior?
  2. Given the mixture representation of t distributions, is there an equivalent for t distributions?
  3. Is there any connection with the equally surprising resolution of the Drton conjecture by Natesh Pillai and Xiao-Li Meng?
  4. In the Khintchine representation, correlated normal variates are created by multiplying a single χ²(3) variate by a vector of uniforms on (-1,1). What are the resulting variates for other degrees of freedomk in the χ²(k) variate?
  5. I also wonder at a connection between this Khintchine representation and the Box-Müller algorithm, as in this earlier X validated question that I turned into an exam problem.

on the origin of the Bayes factor

Posted in Books, Statistics with tags , , , , , , , on November 27, 2015 by xi'an

Alexander Etz and Eric-Jan Wagenmakers from the Department of Psychology of the University of Amsterdam just arXived a paper on the invention of the Bayes factor. In particular, they highlight the role of John Burdon Sanderson (J.B.S.) Haldane in the use of the central tool for Bayesian comparison of hypotheses. In short, Haldane used a Bayes factor before Jeffreys did!

“The idea of a significance test, I suppose, putting half the probability into a constant being 0, and distributing the other half over a range of possible values.”H. Jeffreys

The authors analyse Jeffreys’ 1935 paper on significance tests, which appears to be the very first occurrence of a Bayes factor in his bibliography, testing whether or not two probabilities are equal. They also show the roots of this derivation in earlier papers by Dorothy Wrinch and Harold Jeffreys. [As an “aside”, the early contributions of Dorothy Wrinch to the foundations of 20th Century Bayesian statistics are hardly acknowledged. A shame, when considering they constitute the basis and more of Jeffreys’ 1931 Scientific Inference, Jeffreys who wrote in her necrology “I should like to put on record my appreciation of the substantial contribution she made to [our joint] work, which is the basis of all my later work on scientific inference.” In retrospect, Dorothy Wrinch should have been co-author to this book…] As early as 1919. These early papers by Wrinch and Jeffreys are foundational in that they elaborate a construction of prior distributions that will eventually see the Jeffreys non-informative prior as its final solution [Jeffreys priors that should be called Lhostes priors according to Steve Fienberg, although I think Ernest Lhoste only considered a limited number of transformations in his invariance rule]. The 1921 paper contains de facto the Bayes factor but it does not appear to be advocated as a tool per se for conducting significance tests.

“The historical records suggest that Haldane calculated the first Bayes factor, perhaps almost by accident, before Jeffreys did.” A. Etz and E.J. Wagenmakers

As another interesting aside, the historical account points out that Jeffreys came out in 1931 with what is now called Haldane’s prior for a Binomial proportion, proposed in 1931 (when the paper was read) and in 1932 (when the paper was published in the Mathematical Proceedings of the Cambridge Philosophical Society) by Haldane. The problem tackled by Haldane is again a significance on a Binomial probability. Contrary to the authors, I find the original (quoted) text quite clear, with a prior split before a uniform on [0,½] and a point mass at ½. Haldane uses a posterior odd [of 34.7] to compare both hypotheses but… I see no trace in the quoted material that he ends up using the Bayes factor as such, that is as his decision rule. (I acknowledge decision rule is anachronistic in this setting.) On the side, Haldane also implements model averaging. Hence my reading of this reading of the 1930’s literature is that it remains unclear that Haldane perceived the Bayes factor as a Bayesian [another anachronism] inference tool, upon which [and only which] significance tests could be conducted. That Haldane had a remarkably modern view of splitting the prior according to two orthogonal measures and of correctly deriving the posterior odds is quite clear. With the very neat trick of removing the infinite integral at p=0, an issue that Jeffreys was fighting with at the same time. In conclusion, I would thus rephrase the major finding of this paper as Haldane should get the priority in deriving the Bayesian significance test for point null hypotheses, rather than in deriving the Bayes factor. But this may be my biased views of Bayes factors speaking there…

Another amazing fact I gathered from the historical work of Etz and Wagenmakers is that Haldane and Jeffreys were geographically very close while working on the same problem and hence should have known and referenced their respective works. Which did not happen.

no country for odd means

Posted in Books, Kids, Statistics, University life with tags , , , , , , on November 16, 2015 by xi'an

This morning, Clara Grazian and I arXived a paper about Jeffreys priors for mixtures. This is a part of Clara’s PhD dissertation between Roma and Paris, on which she has worked for the past year. Jeffreys priors cannot be computed analytically for mixtures, which is such a drag that it led us to devise the delayed acceptance algorithm. However, the main message from this detailed study of Jeffreys priors is that they mostly do not work for Gaussian mixture models, in that the posterior is almost invariably improper! This is a definite death knell for Jeffreys priors in this setting, meaning that alternative reference priors, like the one we advocated with Kerrie Mengersen and Mike Titterington, or the similar solution in Roeder and Wasserman, have to be used. [Disclaimer: the title has little to do with the paper, except that posterior means are off for mixtures…]