Archive for Gibbs sampling

common derivation for Metropolis–Hastings and other MCMC algorithms

Posted in Books, pictures, Statistics, Travel, University life with tags , , , , , , , , , , , , on July 25, 2016 by xi'an

Khoa Tran and Robert Kohn from UNSW just arXived a paper on a comprehensive derivation of a large range of MCMC algorithms, beyond Metropolis-Hastings. The idea is to decompose the MCMC move into

  1. a random completion of the current value θ into V;
  2. a deterministic move T from (θ,V) to (ξ,W), where only ξ matters.

If this sounds like a new version of Peter Green’s completion at the core of his 1995 RJMCMC algorithm, it is bedowntown Sydney from under Sydney Harbour bridge, July 15, 2012cause it is indeed essentially the same notion. The resort to this completion allows for a standard form of the Metropolis-Hastings algorithm, which leads to the correct stationary distribution if T is self-inverse. This representation covers Metropolis-Hastings algorithms, Gibbs sampling, Metropolis-within-Gibbs and auxiliary variables methods, slice sampling, recursive proposals, directional sampling, Langevin and Hamiltonian Monte Carlo, NUTS sampling, pseudo-marginal Metropolis-Hastings algorithms, and pseudo-marginal Hamiltonian  Monte Carlo, as discussed by the authors. Given this representation of the Markov chain through a random transform, I wonder if Peter Glynn’s trick mentioned in the previous post on retrospective Monte Carlo applies in this generic setting (as it could considerably improve convergence…)

inefficiency of data augmentation for large samples

Posted in Books, pictures, Running, Statistics, Travel, University life with tags , , , , , , , , , , on May 31, 2016 by xi'an

On Monday, James Johndrow, Aaron Smith, Natesh Pillai, and David Dunson arXived a paper on the diminishing benefits of using data augmentation for large and highly imbalanced categorical data. They reconsider the data augmentation scheme of Tanner and Wong (1987), surprisingly not mentioned, used in the first occurrences of the Gibbs sampler like Albert and Chib’s (1993) or our mixture estimation paper with Jean Diebolt (1990). The central difficulty with data augmentation is that the distribution to be simulated operates on a space that is of order O(n), even when the original distribution covers a single parameter. As illustrated by the coalescent in population genetics (and the subsequent intrusion of the ABC methodology), there are well-known cases when the completion is near to impossible and clearly inefficient (as again illustrated by the failure of importance sampling strategies on the coalescent). The paper provides spectral gaps for the logistic and probit regression completions, which are of order a power of log(n) divided by √n, when all observations are equal to one. In a somewhat related paper with Jim Hobert and Vivek Roy, we studied the spectral gap for mixtures with a small number of observations: I wonder at the existence of a similar result in this setting, when all observations stem from one component of the mixture, when all observations are one. The result in this paper is theoretically appealing, the more because the posteriors associated with such models are highly regular and very close to Gaussian (and hence not that challenging as argued by Chopin and Ridgway). And because the data augmentation algorithm is uniformly ergodic in this setting (as we established with Jean Diebolt  and later explored with Richard Tweedie). As demonstrated in the  experiment produced in the paper, when comparing with HMC and Metropolis-Hastings (same computing times?), which produce much higher effective sample sizes.

the last digit of e

Posted in Kids, Mountains, pictures, Statistics, Travel, University life with tags , , , , , , , on March 3, 2016 by xi'an

Éric Marchand from Sherbrooke, Québec [historical birthplace of MCMC, since Adrian Smith gave his first talk on his Gibbs sampler there, in June 1989], noticed my recent posts about the approximation of e by Monte Carlo methods and sent me a paper he wrote in The Mathematical Gazette of November 1995 [full MCMC era!] about original proofs on the expectation of some stopping rules being e, like the length of increasing runs. And Gnedenko’s uniform summation until exceeding one. Amazing that this simple problem generated so much investigation!!!

twilight zone [of statistics]

Posted in Books, pictures, R, Statistics, University life with tags , , , , , , , , , , on February 26, 2016 by xi'an

mixture with unknown means“I have decided that mixtures, like tequila, are inherently evil and should be avoided at all costs.” L. Wasserman

Larry Wasserman once remarked that finite mixtures were like the twilight zone of statistics, thanks to the numerous idiosyncrasies associated with such models. And George Casella had similar strong reservations about mixture estimation. Avi Feller and co-authors [including Natesh Pillai] have just arXived a paper on this topic, exhibiting shocking (!) properties of the MLE! Their core example is a mixture of two normal distributions with known common variance and known weight different from 0.5, which ensures identifiability. This is a favourite example of mine that we used for instance in our book Introducing Monte Carlo methods with R. If only because we can plot the likelihood and posterior surfaces. (Warning: I wrote those notes on an earlier version of the paper, so mileage may vary in terms of accuracy!)

The “shocking” discovery in the paper is that the MLE is wrong as often as not in selecting the sign of the difference Δ between both means, with an additional accumulation point at zero. The global mode may thus be in the wrong place for small enough sample sizes. And even for larger sizes: when the difference between the means is small the likelihood is likely to be unimodal with a mode quite close to zero. (An interesting remark is that the likelihood derivative is always zero at Δ=0 when considering the special case of both means equal to -Δ and to πΔ/(1-π), respectively, which implies that the overall mean of the mixture is equal to zero. A potential connection with our reparameterisation paper, maybe?)

The alternative proposed by Avi and his co-authors is to proceed through moments, i.e., to revert to Pearson (1892). There are however difficulties with this approach, first and foremost the non-uniqueness of the moment equations used to estimate Δ. For instance, the second cumulant equation chosen by the authors is not always defined as opposed to the third cumulant equation (why not using this third cumulant then). Which does not always produce the right sign… But, in a strange twist, the authors turn those deficiencies into signals for both pathologies (wrong sign and “pile-up” at zero).

“…the grid bootstrap yields an exact p-value for any valid test statistic.”

The most importance issue in this framework being in estimating the parameters, the authors opt for an approach based on tests, which is definitely surprising given the well-known deficiencies of standard tests in mixtures. The test chosen here is a Wald test with a statistic equal to the χ² version of the first cumulant differences. I am surprised that the χ² approximation works in such an unfriendly setting. And I do not understand how the grid is used, unless a certain degree of approximation is accepted, which takes us back to the “dark ages” of imposing a minimal distance Δ to achieve consistency, as in Ghosh and Sen (1985).

muminusmu0 muminusmu1

“..our concern about sign error is trivial in the Bayesian setting: the global mode is simply a poor summary of a multi-modal posterior. More broadly, the weak identification issues we highlight in this paper are not necessarily relevant to a strict Bayesian.”

A priori, I do not think pathologies of the MLE always transfer to Bayes estimators, unless one uses the MAP as an [poor] estimator. But using the MAP is not necessary since posterior means are meaningful in this identified setting, where label switching should not occur. However, running the same experiments with a Gaussian prior on both means and using the posterior mean as my estimator, I did obtain the same pathology of Bayes estimates [also produced in the supplementary material] not concentrating on the true value of the difference, but putting weight on the opposite value and at zero. Using a less standard prior inspired by David Rossell’s talk on non-local priors two weeks ago, which avoids a neighbourhood of zero, I did not get a much different picture as illustrated below:

muminusmux0 muminusmux0

Overall, I remain somewhat uncertain as to what to conclude from this pathological behaviour. When both means are close enough, the sign of the difference is often estimated wrongly. But that could simply mean that the means are not significantly different, for that sample size…

more of the same!

Posted in Books, pictures, Statistics, University life with tags , , , , , , , , on December 10, 2015 by xi'an

aboriginal artist, NGV, Melbourne, July 30, 2012Daniel Seita, Haoyu Chen, and John Canny arXived last week a paper entitled “Fast parallel SAME Gibbs sampling on general discrete Bayesian networks“.  The distributions of the observables are defined by full conditional probability tables on the nodes of a graphical model. The distributions on the latent or missing nodes of the network are multinomial, with Dirichlet priors. To derive the MAP in such models, although this goal is not explicitly stated in the paper till the second page, the authors refer to the recent paper by Zhao et al. (2015), discussed on the ‘Og just as recently, which applies our SAME methodology. Since the paper is mostly computational (and submitted to ICLR 2016, which takes place juuust before AISTATS 2016), I do not have much to comment about it. Except to notice that the authors mention our paper as “Technical report, Statistics and Computing, 2002”. I am not sure the editor of Statistics and Computing will appreciate! The proper reference is in Statistics and Computing, 12:77-84, 2002.

“We argue that SAME is beneficial for Gibbs sampling because it helps to reduce excess variance.”

Still, I am a wee bit surprised at both the above statement and at the comparison with a JAGS implementation. Because SAME augments the number of latent vectors as the number of iterations increases, so should be slower by a mere curse of dimension,, slower than a regular Gibbs with a single latent vector. And because I do not get either the connection with JAGS: SAME could be programmed in JAGS, couldn’t it? If the authors means a regular Gibbs sampler with no latent vector augmentation, the comparison makes little sense as one algorithm aims at the MAP (with a modest five replicas), while the other encompasses the complete posterior distribution. But this sounds unlikely when considering that the larger the number m of replicas the better their alternative to JAGS. It would thus be interesting to understand what the authors mean by JAGS in this setup!

MCMskv, Lenzerheide, 4-7 Jan., 2016 [breaking news #6]

Posted in Kids, Mountains, pictures, Travel, University life with tags , , , , , , , , , , on December 2, 2015 by xi'an

moonriseAs indicated in an earlier MCMskv news, the scientific committee kept a session open for Breaking news! proposals, in conjunction with poster submissions. We received 21 proposals and managed to squeeze 12 fifteen minute presentations in an already tight program. (I advise all participants to take a relaxing New Year break and to load in vitamins and such in preparation for a 24/7 or rather 24/3 relentless and X’citing conference!) Here are the selected presentations, with (some links to my posts on the related papers and) abstracts available on the conference website. Note to all participants that there are still a few days left for submitting posters!

Luke Bornn

Jon Cockayne

Gersende Fort

Michael Gutmann

James Johndrow

Jean-Michel Marin

Murray Pollock

Maxim Rabinovich

Rebecca Steorts

Alexander Terenin

Yazhen Wang

Giacomo Zanella

data augmentation with divergence

Posted in Books, Kids, Statistics, University life with tags , , , , , on November 18, 2015 by xi'an

Another (!) Cross Validated question that shed some light on the difficulties of explaining the convergence of MCMC algorithms. Or in understanding conditioning and hierarchical models. The author wanted to know why a data augmentation of his did not converge: In a simplified setting, given an observation y that he wrote as y=h(x,θ), he had built a Gibbs sampler by reconstructing x=g(y,θ) and simulating θ given x: at each iteration t,

  1. compute xt=g(y,θt-1)
  2. simulate θt~π(θ|xt)

and he attributed the lack of convergence to a possible difficulty with the Jacobian. My own interpretation of the issue was rather that condition on the unobserved x was not the same as conditioning on the observed y and hence that y was missing from step 2. And that the simulation of x is useless. Unless one uses it in an augmented scheme à la Xiao-Li… Nonetheless, I like the problem, if only because my very first reaction was to draw a hierarchical dependence graph and to conclude this should be correct, before checking on a toy example that it was not!


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