unbiased consistent nested sampling via sequential Monte Carlo

Posted in pictures, Statistics, Travel with tags , , , , , , , , on June 12, 2018 by xi'an

“Moreover, estimates of the marginal likelihood are unbiased.” (p.2)

Rob Salomone, Leah South, Chris Drovandi and Dirk Kroese (from QUT and UQ, Brisbane) recently arXived a paper that frames the nested sampling in such a way that marginal likelihoods can be unbiasedly (and consistently) estimated.

“Why isn’t nested sampling more popular with statisticians?” (p.7)

A most interesting question, especially given its popularity in cosmology and other branches of physics. A first drawback pointed out in the c is the requirement of independence between the elements of the sample produced at each iteration. Which never occurred as the number one difficulty there, as the simplest implementation runs a Markov chain from the last removed entry, independently from the remaining entries. Even stationarity is not an issue since I believe that the first occurrence within the level set is distributed from the constrained prior.

A second difficulty is the use of quadrature which turns integrand into step functions at random slices. Indeed, mixing Monte Carlo with numerical integration makes life much harder, as shown by the early avatars of nested sampling that only accounted for the numerical errors. (And which caused Nicolas and I to write our critical paper in Biometrika.) There are few studies of that kind in the literature, the only one I can think of being [my former PhD student] Anne Philippe‘s thesis twenty years ago.

The third issue stands with the difficulty in parallelising the method. Except by jumping k points at once, rather than going one level at a time. While I agree this makes life more complicated, I am also unsure about the severity of that issue as k nested sampling algorithms can be run in parallel and aggregated in the end, from simple averaging to something more elaborate.

The final blemish is that the nested sampling estimator has a stopping mechanism that induces a truncation error, again maybe a lesser problem given the overall difficulty in assessing the total error.

The paper takes advantage of the ability of SMC to produce unbiased estimates of a sequence of normalising constants (or of the normalising constants of a sequence of targets). For nested sampling, the sequence is made of the prior distribution restricted to an embedded sequence of level sets. With another sequence restricted to bands (likelihood between two likelihood boundaries). If all restricted posteriors of the second kind and their normalising constant are known, the full posterior is known. Apparently up to the main normalising constant, i.e. the marginal likelihood., , except that it is also the sum of all normalising constants. Handling this sequence by SMC addresses the four concerns of the four authors, apart from the truncation issue, since the largest likelihood bound need be set for running the algorithm.

When the sequence of likelihood bounds is chosen based on the observed likelihoods so far, the method becomes adaptive. Requiring again the choice of a stopping rule that may induce bias if stopping occurs too early. And then, in a twist that is not clearly explained in the paper, the focus moves to an improved nested sampler that moves one likelihood value at a time, with a particle step replacing a single particle. (Things get complicated when several particles may take the very same likelihood value, but randomisation helps.) At this stage the algorithm is quite similar to the original nested sampler. Except for the unbiased estimation of the constants, the final constant, and the replacement of exponential weights exp(-t/N) by powers of (N-1/N).

The remainder of this long paper (61 pages!) is dedicated to practical implementation, calibration and running a series of comparisons. A nice final touch is the thanks to the ‘Og for its series of posts on nested sampling, which “helped influence this work, and played a large part in inspiring it.”

In conclusion, this paper is certainly a worthy exploration of the nested sampler, providing further arguments towards a consistent version, with first and foremost an (almost?) unbiased resolution. The comparison with a wide range of alternatives remains open, in particular time-wise, if evidence is the sole target of the simulation. For instance, the choice of this sequence of targets in an SMC may be improved by another sequence, since changing one particle at a time does not sound efficient. The complexity of the implementation and in particular of the simulation from the prior under more and more stringent constraints need to be addressed.

another version of the corrected harmonic mean estimator

Posted in Books, pictures, Statistics, University life with tags , , , , , on June 11, 2018 by xi'an

A few days ago I came across a short paper in the Central European Journal of Economic Modelling and Econometrics by Pajor and Osiewalski that proposes a correction to the infamous harmonic mean estimator that is essentially the one Darren and I made in 2009, namely to restrict the evaluations of the likelihood function to a subset A of the simulations from the posterior. Paper that relates to an earlier 2009 paper by Peter Lenk, which investigates the same object with this same proposal and that we had missed for all that time. The difference is that, while we examine an arbitrary HPD region at level 50% or 80% as the subset A, Lenk proposes to derive a minimum likelihood value from the MCMC run and to use the associated HPD region, which means using all simulations, hence producing the same object as the original harmonic mean estimator, except that it is corrected by a multiplicative factor P(A). Or rather an approximation. This correction thus maintains the infinite variance of the original, a point apparently missed in the paper.

Toukoul, Brussels

Posted in pictures, Travel with tags , , , , , , , , on June 10, 2018 by xi'an

While in Brussels this week, I realised I was staying near a well-rated Ethiopian restaurant called Toukoul (from the name of a, Ethiopian hut) and went there early enough to secure a table before it got full. For plenty of good reasons as the food is terrific, with enough spice for the taste

to linger in the mouth long after the dish is gone. (Contrary to the few Ethiopian restaurants I tested in the past months.) And plenty of injera available on the table. And a highly friendly service. A place to remember for future trips to Brussels. Definitely! 

Brussels snapshot [jatp]

Posted in Statistics with tags , , , , , , , , , on June 9, 2018 by xi'an

maximal spacing around order statistics [#2]

Posted in Books, R, Statistics, University life with tags , , , , , , , , on June 8, 2018 by xi'an

The proposed solution of the riddle from the Riddler discussed here a few weeks ago is rather approximative, in that the distribution of


when the n-sample is made of iid Normal variates is (a) replaced with the distribution of one arbitrary minimum and (b) the distribution of the minimum is based on an assumption of independence between the absolute differences. Which does not hold, as shown by the above correlation matrix (plotted via corrplot) for N=11 and 10⁴ simulations. One could think that this correlation decreases with N, but it remains essentially 0.2 for larger values of N. (On the other hand, the minima are essentially independent.)

are there a frequentist and a Bayesian likelihoods?

Posted in Statistics with tags , , , , , , , , , , on June 7, 2018 by xi'an

A question that came up on X validated and led me to spot rather poor entries in Wikipedia about both the likelihood function and Bayes’ Theorem. Where unnecessary and confusing distinctions are made between the frequentist and Bayesian versions of these notions. I have already discussed the later (Bayes’ theorem) a fair amount here. The discussion about the likelihood is quite bemusing, in that the likelihood function is the … function of the parameter equal to the density indexed by this parameter at the observed value.

“What we can find from a sample is the likelihood of any particular value of r, if we define the likelihood as a quantity proportional to the probability that, from a population having the particular value of r, a sample having the observed value of r, should be obtained.” R.A. Fisher, On the “probable error’’ of a coefficient of correlation deduced from a small sample. Metron 1, 1921, p.24

By mentioning an informal side to likelihood (rather than to likelihood function), and then stating that the likelihood is not a probability in the frequentist version but a probability in the Bayesian version, the W page makes a complete and unnecessary mess. Whoever is ready to rewrite this introduction is more than welcome! (Which reminded me of an earlier question also on X validated asking why a common reference measure was needed to define a likelihood function.)

This also led me to read a recent paper by Alexander Etz, whom I met at E.J. Wagenmakers‘ lab in Amsterdam a few years ago. Following Fisher, as Jeffreys complained about

“..likelihood, a convenient term introduced by Professor R.A. Fisher, though in his usage it is sometimes multiplied by a constant factor. This is the probability of the observations given the original information and the hypothesis under discussion.” H. Jeffreys, Theory of Probability, 1939, p.28

Alexander defines the likelihood up to a constant, which causes extra-confusion, for free!, as there is no foundational reason to introduce this degree of freedom rather than imposing an exact equality with the density of the data (albeit with an arbitrary choice of dominating measure, never neglect the dominating measure!). The paper also repeats the message that the likelihood is not a probability (density, missing in the paper). And provides intuitions about maximum likelihood, likelihood ratio and Wald tests. But does not venture into a separate definition of the likelihood, being satisfied with the fundamental notion to be plugged into the magical formula


Metropolis-Hastings importance sampling

Posted in Books, Statistics, University life with tags , , , , , , , , , on June 6, 2018 by xi'an

[Warning: As I first got the paper from the authors and sent them my comments, this paper read contains their reply as well.]

In a sort of crazy coincidence, Daniel Rudolf and Björn Sprungk arXived a paper on a Metropolis-Hastings importance sampling estimator that offers similarities with  the one by Ingmar Schuster and Ilja Klebanov posted on arXiv the same day. The major difference in the construction of the importance sampler is that Rudolf and Sprungk use the conditional distribution of the proposal in the denominator of their importance weight, while Schuster and Klebanov go for the marginal (or a Rao-Blackwell representation of the marginal), mostly in an independent Metropolis-Hastings setting (for convergence) and for a discretised Langevin version in the applications. The former use a very functional L² approach to convergence (which reminded me of the early Schervish and Carlin, 1990, paper on the convergence of MCMC algorithms), not all of it necessary in my opinion. As for instance the extension of convergence properties to the augmented chain, namely (current, proposed), is rather straightforward since the proposed chain is a random transform of the current chain. An interesting remark at the end of the proof of the CLT is that the asymptotic variance of the importance sampling estimator is the same as with iid realisations from the target. This is a point we also noticed when constructing population Monte Carlo techniques (more than ten years ago), namely that dependence on the past in sequential Monte Carlo does not impact the validation and the moments of the resulting estimators, simply because “everything cancels” in importance ratios. The mean square error bound on the Monte Carlo error (Theorem 20) is not very surprising as the term ρ(y)²/P(x,y) appears naturally in the variance of importance samplers.

The first illustration where the importance sampler does worse than the initial MCMC estimator for a wide range of acceptance probabilities (Figures 2 and 3, which is which?) and I do not understand the opposite conclusion from the authors.

[Here is an answer from Daniel and Björn about this point:]

Indeed the formulation in our paper is unfortunate. The point we want to stress is that we observed in the numerical experiments certain ranges of step-sizes for which MH importance sampling shows a better performance than the classical MH algorithm with optimal scaling. Meaning that the MH importance sampling with optimal step-size can outperform MH sampling, without using additional computational resources. Surprisingly, the optimal step-size for the MH importance sampling estimator seems to remain constant for an increasing dimension in contrast to the well-known optimal scaling of the MH algorithm (given by a constant optimal acceptance rate).

The second uses the Pima Indian diabetes benchmark, amusingly (?) referring to Chopin and Ridgway (2017) who warn against the recourse to this dataset and to this model! The loss in mean square error due to the importance sampling may again be massive (Figure 5) and setting for an optimisation of the scaling factor in Metropolis-Hastings algorithms sounds unrealistic.

[And another answer from Daniel and Björn about this point:]

Indeed, Chopin and Ridgway suggest more complex problems with a larger number of covariates as benchmarks. However, the well-studied PIMA data set is a sufficient example in order to illustrate the possible benefits but also the limitations of the MH importance sampling approach. The latter are clearly (a) the required knowledge about the optimal step-size—otherwise the performance can indeed be dramatically worse than for the MH algorithm—and (b) the restriction to a small or at most moderate number of covariates. As you are indicating, optimizing the scaling factor is a challenging task. However, the hope is to derive some simple rule of thumb for the MH importance sampler similar to the well-known acceptance rate tuning for the standard MCMC estimator.