A new arXival on nested sampling: “A statistical test for nested sampling algorithms” by Johannes Buchner. The point of the test is to check if versions of the nested sampling algorithm that fail to guarantee increased likelihood (or nesting) at each step are not missing parts of the posterior mass. and hence producing biased evidence approximations. This applies to MultiNest for instance. This version of nest sampling evaluates the above-threshold region by drawing hyper-balls around the remaining points. A solution which is known to fail in one specific but meaningful case. Buchner’s arXived paper proposes an hyper-pyramid distribution for which the volume of any likelihood constrained set is known. Hence allowing for a distribution test like Kolmogorov-Smirnov. Confirming the findings of Beaujean and Caldwell (2013). The author then proposes an alternative to MultiNest that is more robust but also much more costly as it computes distances between all pairs of bootstrapped samples. This solution passes the so-called “shrinkage test”, but it is orders of magnitude less efficient than MultiNest. And also simply shows that its coverage is fine for a specific target rather than all possible targets. I wonder if a solution to the problem is at all possible given that evaluating a support or a convex hull is a complex problem which complexity explodes with the dimension.
Archive for evidence
L’auteur du texte que j’ai à traduire désigne les facteurs de Bayes comme une “Bayesian measure of evidence”, et les tests de p-value comme une “frequentist measure of evidence”. Je me demandais s’il existait une traduction française reconnue et établie pour cette expression de “measure of evidence”. J’ai rencontré parfois “mesure d’évidence” qui ressemble fort à un anglicisme, et parfois “estimateur de preuve”, mais qui me semble pouvoir mener à des confusions avec d’autres emploi du terme “estimateur”.
which (pardon my French!) wonders how to translate the term evidence into French. It would sound natural that the French évidence is the answer but this is not the case. Despite sharing the same Latin root (evidentia), since the English version comes from medieval French, the two words have different meanings: in English, it means a collection of facts coming to support an assumption or a theory, while in French it means something obvious, which truth is immediately perceived. Surprisingly, English kept the adjective evident with the same [obvious] meaning as the French évident. But the noun moved towards a much less definitive meaning, both in Law and in Science. I had never thought of the huge gap between the two meanings but must have been surprised at its use the first time I heard it in English. But does not think about it any longer, as when I reviewed Seber’s Evidence and Evolution.
One may wonder at the best possible translation of evidence into French. Even though marginal likelihood (vraisemblance marginale) is just fine for statistical purposes. I would suggest faisceau de présomptions or degré de soutien or yet intensité de soupçon as (lengthy) solutions. Soupçon could work as such, but has a fairly negative ring…
Just before Christmas (a year ago), John Birge, Changgee Chang, and Nick Polson arXived a paper with the above title. Split sampling is presented a a tool conceived to handle rare event probabilities, written in this paper as
where π is the prior and L the likelihood, m being a large enough bound to make the probability small. However, given John Skilling’s representation of the marginal likelihood as the integral of the Z(m)’s, this simulation technique also applies to the approximation of the evidence. The paper refers from the start to nested sampling as a motivation for this method, presumably not as a way to run nested sampling, which was created as a tool for evidence evaluation, but as a competitor. Nested sampling may indeed face difficulties in handling the coverage of the higher likelihood regions under the prior and it is an approximative method, as we detailed in our earlier paper with Nicolas Chopin. The difference between nested and split sampling is that split sampling adds a distribution ω(m) on the likelihood levels m. If pairs (x,m) can be efficiently generated by MCMC for the target
the marginal density of m can then be approximated by Rao-Blackwellisation. From which the authors derive an estimate of Z(m), since the marginal is actually proportional to ω(m)Z(m). (Because of the Rao-Blackwell argument, I wonder how much this differs from Chib’s 1995 method, i.e. if the split sampling estimator could be expressed as a special case of Chib’s estimator.) The resulting estimator of the marginal also requires a choice of ω(m) such that the associated cdf can be computed analytically. More generally, the choice of ω(m) impacts the quality of the approximation since it determines how often and easily high likelihood regions will be hit. Note also that the conditional π(x|m) is the same as in nested sampling, hence may run into difficulties for complex likelihoods or large datasets.
When reading the beginning of the paper, the remark that “the chain will visit each level roughly uniformly” (p.13) made me wonder at a possible correspondence with the Wang-Landau estimator. Until I read the reference to Jacob and Ryder (2012) on page 16. Once again, I wonder at a stronger link between both papers since the Wang-Landau approach aims at optimising the exploration of the simulation space towards a flat histogram. See for instance Figure 2.
The following part of the paper draws a comparison with both nested sampling and the product estimator of Fishman (1994). I do not fully understand the consequences of the equivalence between those estimators and the split sampling estimator for specific choices of the weight function ω(m). Indeed, it seemed to me that the main point was to draw from a joint density on (x,m) to avoid the difficulties of exploring separately each level set. And also avoiding the approximation issues of nested sampling. As a side remark, the fact that the harmonic mean estimator occurs at several points of the paper makes me worried. The qualification of “poor Monte Carlo error variances properties” is an understatement for the harmonic mean estimator, as it generally has infinite variance and it hence should not be used at all, even as a starting point. The paper does not elaborate much about the cross-entropy method, despite using an example from Rubinstein and Kroese (2004).
In conclusion, an interesting paper that made me think anew about the nested sampling approach, which keeps its fascination over the years! I will most likely use it to build an MSc thesis project this summer in Warwick.
In yet another permutation of the original title (!), Andrew Gelman posted the answer Val Johnson sent him after our (submitted) letter to PNAS. As Val did not send me a copy (although Andrew did!), I will not reproduce it here and I rather refer the interested readers to Andrews’ blog… In addition to Andrew’s (sensible) points, here are a few idle (post-X’mas and pre-skiing) reflections:
- “evidence against a false null hypothesis accrues exponentially fast” makes me wonder in which metric this exponential rate (in γ?) occurs;
- that “most decision-theoretic analyses of the optimal threshold to use for declaring a significant finding would lead to evidence thresholds that are substantially greater than 5 (and probably also greater 25)” is difficult to accept as an argument since there is no trace of a decision-theoretic argument in the whole paper;
- Val rejects our minimaxity argument on the basis that “[UMPBTs] do not involve minimization of maximum loss” but the prior that corresponds to those tests is minimising the integrated probability of not rejecting at threshold level γ, a loss function integrated against parameter and observation, a Bayes risk in other words… Point masses or spike priors are clearly characteristics of minimax priors. Furthermore, the additional argument that “in most applications, however, a unique loss function/prior distribution combination does not exist” has been used by many to refute the Bayesian perspective and makes me wonder what are the arguments left in using a (pseudo-)Bayesian approach;
- the next paragraph is pure tautology: the fact that “no other test, based on either a subjectively or objectively specified alternative hypothesis, is as likely to produce a Bayes factor that exceeds the specified evidence threshold” is a paraphrase of the definition of UMPBTs, not an argument. I do not see we should solely “worry about false negatives”, since minimising those should lead to a point mass on the null (or, more seriously, should not lead to the minimax-like selection of the prior under the alternative).
Jeong Eun (Kate) Lee and I completed this paper, “Importance sampling schemes for evidence approximation in mixture models“, now posted on arXiv. (With the customary one-day lag for posting, making me bemoan the days of yore when arXiv would give a definitive arXiv number at the time of submission.) Kate came twice to Paris in the past years to work with me on this evaluation of Chib’s original marginal likelihood estimate (also called the candidate formula by Julian Besag). And on the improvement proposed by Berkhof, van Mechelen, and Gelman (2003), based on averaging over all permutations, idea that we rediscovered in an earlier paper with Jean-Michel Marin. (And that Andrew seemed to have completely forgotten. Despite being the very first one to publish [in English] a paper on a Gibbs sampler for mixtures.) Given that this averaging can get quite costly, we propose a preliminary step to reduce the number of relevant permutations to be considered in the averaging, removing far-away modes that do not contribute to the Rao-Blackwell estimate and called dual importance sampling. We also considered modelling the posterior as a product of k-component mixtures on the components, following a vague idea I had in the back of my mind for many years, but it did not help. In the above boxplot comparison of estimators, the marginal likelihood estimators are
- Chib’s method using T = 5000 samples with a permutation correction by multiplying by k!.
- Chib’s method (1), using T = 5000 samples which are randomly permuted.
- Importance sampling estimate (7), using the maximum likelihood estimate (MLE) of the latents as centre.
- Dual importance sampling using q in (8).
- Dual importance sampling using an approximate in (14).
- Bridge sampling (3). Here, label switching is imposed in hyperparameters.