Archive for evidence

reading classics (The End)

Posted in Books, Kids, Statistics, University life with tags , , , , , , , , , on February 24, 2015 by xi'an

La Défense from Paris-Dauphine, Nov. 15, 2012Today was the final session of our Reading Classics Seminar for the academic year 2014-2015. I have not reported on this seminar much so far because it has had starting problems, namely hardly any student present on the first classes and therefore several re-starts until we reached a small group of interested students. And this is truly The End for this enjoyable experiment as this is the final year for my TSI Master at Paris-Dauphine, as it will become integrated within the new MASH Master next year.

As a last presentation for the entire series, my student picked John Skilling’s Nested Sampling, not that it was in my list of “classics”, but he had worked on the paper in a summer project and was thus reasonably fluent with the topic. As he did a good enough job (!), here are his slides.

Some of the questions that came to me during the talk were on how to run nested sampling sequentially, both in the data and in the number of simulated points, and on incorporating more deterministic moves in order to remove some of the Monte Carlo variability. I was about to ask about (!) the Hamiltonian version of nested sampling but then he mentioned his last summer internship on this very topic! I also realised during that talk that the formula (for positive random variables)

\int_0^\infty(1-F(x))\text{d}x = \mathbb{E}_F[X]

does not require absolute continuity of the distribution F.

ABC by population annealing

Posted in Statistics, University life with tags , , , , , , , , on January 6, 2015 by xi'an

The paper “Bayesian Parameter Inference and Model Selection by Population Annealing in System Biology” by Yohei Murakami got published in PLoS One last August but I only became aware of it when ResearchGate pointed it out to me [by mentioning one of our ABC papers was quoted there].

“We are recommended to try a number of annealing schedules to check the influence of the schedules on the simulated data (…) As a whole, the simulations with the posterior parameter ensemble could, not only reproduce the data used for parameter inference, but also capture and predict the data which was not used for parameter inference.”

Population annealing is a notion introduced by Y Iba, the very same IBA who introduced the notion of population Monte Carlo that we studied in subsequent papers. It reproduces the setting found in many particle filter papers of a sequence of (annealed or rather tempered) targets ranging from an easy (i.e., almost flat) target to the genuine target, and of an update of a particle set by MCMC moves and reweighing. I actually have trouble perceiving the difference with other sequential Monte Carlo schemes as those exposed in Del Moral, Doucet and Jasra (2006, Series B). And the same is true of the ABC extension covered in this paper. (Where the annealed intermediate targets correspond to larger tolerances.) This sounds like a traditional ABC-SMC algorithm. Without the adaptive scheme on the tolerance ε found e.g. in Del Moral et al., since the sequence is set in advance. [However, the discussion about the implementation includes the above quote that suggests a vague form of cross-validated tolerance construction]. The approximation of the marginal likelihood also sounds standard, the marginal being approximated by the proportion of accepted pseudo-samples. Or more exactly by the sum of the SMC weights at the end of the annealing simulation. This actually raises several questions: (a) this estimator is always between 0 and 1, while the marginal likelihood is not restricted [but this is due to a missing 1/ε in the likelihood estimate that cancels from both numerator and denominator]; (b) seeing the kernel as a non-parametric estimate of the likelihood led me to wonder why different ε could not be used in different models, in that the pseudo-data used for each model under comparison differs. If we were in a genuine non-parametric setting the bandwidth would be derived from the pseudo-data.

“Thus, Bayesian model selection by population annealing is valid.”

The discussion about the use of ABC population annealing somewhat misses the point of using ABC, which is to approximate the genuine posterior distribution, to wit the above quote: that the ABC Bayes factors favour the correct model in the simulation does not tell anything about the degree of approximation wrt the original Bayes factor. [The issue of non-consistent Bayes factors does not apply here as there is no summary statistic applied to the few observations in the data.] Further, the magnitude of the variability of the values of this Bayes factor as ε varies, from 1.3 to 9.6, mostly indicates that the numerical value is difficult to trust. (I also fail to explain the huge jump in Monte Carlo variability from 0.09 to 1.17 in Table 1.) That this form of ABC-SMC improves upon the basic ABC rejection approach is clear. However it needs to build some self-control to avoid arbitrary calibration steps and reduce the instability of the final estimates.

“The weighting function is set to be large value when the observed data and the simulated data are ‘‘close’’, small value when they are ‘‘distant’’, and constant when they are ‘‘equal’’.”

The above quote is somewhat surprising as the estimated likelihood f(xobs|xobs,θ) is naturally constant when xobs=xsim… I also failed to understand how the model intervened in the indicator function used as a default ABC kernel

importance sampling schemes for evidence approximation [revised]

Posted in Statistics, University life with tags , , , , , , , on November 18, 2014 by xi'an

After a rather intense period of new simulations and versions, Juong Een (Kate) Lee and I have now resubmitted our paper on (some) importance sampling schemes for evidence approximation in mixture models to Bayesian Analysis. There is no fundamental change in the new version but rather a more detailed description of what those importance schemes mean in practice. The original idea in the paper is to improve upon the Rao-Blackwellisation solution proposed by Berkoff et al. (2002) and later by Marin et al. (2005) to avoid the impact of label switching on Chib’s formula. The Rao-Blackwellisation consists in averaging over all permutations of the labels while the improvement relies on the elimination of useless permutations, namely those that produce a negligible conditional density in Chib’s (candidate’s) formula. While the improvement implies truncated the overall sum and hence induces a potential bias (which was the concern of one referee), the determination of the irrelevant permutations after relabelling next to a single mode does not appear to cause any bias, while reducing the computational overload. Referees also made us aware of many recent proposals that conduct to different evidence approximations, albeit not directly related with our purpose. (One was Rodrigues and Walker, 2014, discussed and commented in a recent post.)

independent component analysis and p-values

Posted in pictures, Running, Statistics, Travel, University life with tags , , , , , , , , , , , , , on September 8, 2014 by xi'an

WariseLast morning at the neuroscience workshop Jean-François Cardoso presented independent component analysis though a highly pedagogical and enjoyable tutorial that stressed the geometric meaning of the approach, summarised by the notion that the (ICA) decomposition


of the data X seeks both independence between the columns of S and non-Gaussianity. That is, getting as away from Gaussianity as possible. The geometric bits came from looking at the Kullback-Leibler decomposition of the log likelihood

-\mathbb{E}[\log L(\theta|X)] = KL(P,Q_\theta) + \mathfrak{E}(P)

where the expectation is computed under the true distribution P of the data X. And Qθ is the hypothesised distribution. A fine property of this decomposition is a statistical version of Pythagoreas’ theorem, namely that when the family of Qθ‘s is an exponential family, the Kullback-Leibler distance decomposes into

KL(P,Q_\theta) = KL(P,Q_{\theta^0}) + KL(Q_{\theta^0},Q_\theta)

where θ⁰ is the expected maximum likelihood estimator of θ. (We also noticed this possibility of a decomposition in our Kullback-projection variable-selection paper with Jérôme Dupuis.) The talk by Aapo Hyvärinen this morning was related to Jean-François’ in that it used ICA all the way to a three-level representation if oriented towards natural vision modelling in connection with his book and the paper on unormalised models recently discussed on the ‘Og.

On the afternoon, Eric-Jan Wagenmaker [who persistently and rationally fight the (ab)use of p-values and who frequently figures on Andrew’s blog] gave a warning tutorial talk about the dangers of trusting p-values and going fishing for significance in existing studies, much in the spirit of Andrew’s blog (except for the defence of Bayes factors). Arguing in favour of preregistration. The talk was full of illustrations from psychology. And included the line that ESP testing is the jester of academia, meaning that testing for whatever form of ESP should be encouraged as a way to check testing procedures. If a procedure finds a significant departure from the null in this setting, there is something wrong with it! I was then reminded that Eric-Jan was one of the authors having analysed Bem’s controversial (!) paper on the “anomalous processes of information or energy transfer that are currently unexplained in terms of known physical or biological mechanisms”… (And of the shocking talk by Jessica Utts on the same topic I attended in Australia two years ago.)

a statistical test for nested sampling

Posted in Books, Statistics, University life with tags , , , , , on July 25, 2014 by xi'an

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.

how to translate evidence into French?

Posted in Books, Statistics, University life with tags , , , , , on July 6, 2014 by xi'an

I got this email from Gauvain who writes a PhD in philosophy of sciences a few minutes ago:

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…

Split Sampling: expectations, normalisation and rare events

Posted in Books, Statistics, University life with tags , , , , , , on January 27, 2014 by xi'an

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.


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