**T**oday I am taking part in a one-day workshop at the Université of Clermont Auvergne on ABC. With applications to cosmostatistics, along with Martin Kilbinger [with whom I worked on PMC schemes], Florent Leclerc and Grégoire Aufort. This should prove a most exciting day! (With not enough time to run up Puy de Dôme in the morning, though.)

## Archive for PMC

## ABC in Clermont-Ferrand

Posted in Mountains, pictures, Statistics, Travel, University life with tags ABC, ABC-Gibbs, Approximate Bayesian computation, Auvergne, Clermont-Ferrand, conditional sufficiency, cosmostats, dimension reduction, Gibbs sampling, likelihood-free methods, PMC, volcano on September 20, 2019 by xi'an## SMC on a sequence of increasing dimension targets

Posted in Statistics with tags birth-and-death process, finite mixtures, Jacobian, MCMC, PMC, population Monte Carlo, reversible jump MCMC, sequential Monte Carlo, simulated annealing, SMC on February 15, 2017 by xi'an**R**ichard Everitt and co-authors have arXived a preliminary version of a paper entitled *Sequential* *Bayesian inference for mixture models and the coalescent using sequential Monte Carlo samplers with transformations*. The central notion is an SMC version of the Carlin & Chib (1995) completion in the comparison of models in different dimensions. Namely to create auxiliary variables for each model in such a way that the dimension of the completed models are all the same. (Reversible jump MCMC à la Peter Green (1995) can also be interpreted this way, even though only relevant bits of the completion are used in the transitions.) I find the paper and the topic most interesting if only because it relates to earlier papers of us on population Monte Carlo. It also brought to my awareness the paper by Karagiannis and Andrieu (2013) on annealed reversible jump MCMC that I had missed at the time it appeared. The current paper exploits this annealed expansion in the devising of the moves. (Sequential Monte Carlo on a sequence of models with increasing dimension has been studied in the past.)

The way the SMC is described in the paper, namely, reweight-subsample-move, does not strike me as the most efficient as I would try to instead move-reweight-subsample, using a relevant move that incorporate the new model and hence enhance the chances of not rejecting.

One central application of the paper is mixture models with an unknown number of components. The SMC approach applied to this problem means creating a new component at each iteration t and moving the existing particles after adding the parameters of the new component. Since using the prior for this new part is unlikely to be at all efficient, a split move as in Richardson and Green (1997) can be considered, which brings back the dreaded Jacobian of RJMCMC into the picture! Here comes an interesting caveat of the method, namely that the split move forces a choice of the split component of the mixture. However, this does not appear as a strong difficulty, solved in the paper by auxiliary [index] variables, but possibly better solved by a mixture representation of the proposal, as in our PMC [population Monte Carlo] papers. Which also develop a family of SMC algorithms, incidentally. We found there that using a mixture representation of the proposal achieves a provable variance reduction.

“This puts a requirement on TSMC that the single transition it makes must be successful.”

As pointed by the authors, the transformation SMC they develop faces the drawback that a given model is only explored once in the algorithm, when moving to the next model. On principle, there would be nothing wrong in including regret steps, retracing earlier models in the light of the current one, since each step is an importance sampling step valid on its own right. But SMC also offers a natural albeit potentially high-varianced approximation to the marginal likelihood, which is quite appealing when comparing with an MCMC outcome. However, it would have been nice to see a comparison with alternative estimates of the marginal in the case of mixtures of distributions. I also wonder at the comparative performances of a dual approach that would be sequential in the number of observations as well, as in Chopin (2004) or our first population Monte Carlo paper (Cappé et al., 2005), since subsamples lead to tempered versions of the target and hence facilitate moves between models, being associated with flatter likelihoods.

## PMC for combinatoric spaces

Posted in Statistics, University life with tags AMIS, CUNY, importance sampling, Monte Carlo Statistical Methods, PMC, population Monte Carlo, simulation, unbiasedness on July 28, 2014 by xi'an**I** received this interesting [edited] email from Xiannian Fan at CUNY:

I am trying to use PMC to solve Bayesian network structure learning problem (which is in a combinatorial space, not continuous space).

In PMC, the proposal distributions q

_{i,t}can be very flexible, even specific to each iteration and each instance. My problem occurs due to the combinatorial space.For importance sampling, the requirement for proposal distribution, q, is:

support (p) ⊂ support (q) (*)

For PMC, what is the support of the proposal distribution in iteration t? is it

support (p) ⊂ U support(q

_{i,t}) (**)or does (*) apply to every q

_{i,t}?For continuous problem, this is not a big issue. We can use random walk of Normal distribution to do local move satisfying (*). But for combination search, local moving only result in finite states choice, just not satisfying (*). For example for a permutation (1,3,2,4), random swap has only choose(4,2)=6 neighbor states.

**F**airly interesting question about population Monte Carlo (PMC), a sequential version of importance sampling we worked on with French colleagues in the early 2000’s. (The name population Monte Carlo comes from Iba, 2000.) While MCMC samplers do not have to cover the whole support of p at each iteration, it is much harder for importance samplers as their core justification is to provide an unbiased estimator to for all integrals of interest. Thus, when using the PMC estimate,

1/n ∑_{i,t} {p(x_{i,t})/q_{i,t}(x_{i,t})}h(q_{i,t}), x_{i,t~}q_{i,t(x})

this estimator is only unbiased when the supports of the q_{i,t }“s are all containing the support of p. The only other cases I can think of are

- associating the q
_{i,t }“s with a partition S_{i,t}of the support of p and using instead∑

_{i,t}{p(x_{i,t})/q_{i,t}(x_{i,t})}h(q_{i,t}), x_{i,t~}q_{i,t(x}) - resorting to AMIS under the assumption (**) and using instead
1/n ∑

_{i,t}{p(x_{i,t})/∑_{j,t}q_{j,t}(x_{i,t})}h(q_{i,t}), x_{i,t~}q_{i,t(x})

but I am open to further suggestions!

## Initializing adaptive importance sampling with Markov chains

Posted in Statistics with tags AMIS, arXiv, cosmoPMC, evidence, Kullback, marginal likelihood, Multinest, nested sampling, PMC, population Monte Carlo, sequential Monte Carlo, simulation on May 6, 2013 by xi'an**A**nother paper recently arXived by Beaujean and Caldwell elaborated on our population Monte Carlo papers (Cappé et al., 2005, Douc et al., 2007, Wraith et al., 2010) to design a more thorough starting distribution. Interestingly, the authors mention the fact that PMC is an EM-type algorithm to emphasize the importance of the starting distribution, as with “poor proposal, PMC fails as proposal updates lead to a consecutively poorer approximation of the target” (p.2). I had not thought of this possible feature of PMC, which indeed proceeds along integrated EM steps, and thus could converge to a local optimum (if not poorer than the start as the Kullback-Leibler divergence decreases).

**T**he solution proposed in this paper is similar to the one we developed in our AMIS paper. An important part of the simulation is dedicated to the construction of the starting distribution, which is a mixture deduced from multiple Metropolis-Hastings runs. I find the method spends an unnecessary long time on refining this mixture by culling the number of components: down-the-shelf clustering techniques should be sufficient, esp. if one considers that *the value of the target is available at every simulated point*. This has been my pet (if idle) theory for a long while: we do not take (enough) advantage of this informative feature in our simulation methods… I also find the *Student’s t versus Gaussian kernel* debate (p.6) somehow superfluous: as we shown in Douc et al., 2007, we can process Student’s *t* distributions so we can as well work with those. And rather worry about the homogeneity assumption this choice implies: working with any elliptically symmetric kernel assumes a local Euclidean structure on the parameter space, for all components, and does not model properly highly curved spaces. Another pet theory of mine’s. As for picking the necessary number of simulations at each PMC iteration, I would add to the ESS and the survival rate of the components a measure of the Kullback-Leibler divergence, as it *should decrease* at each iteration (with an infinite number of particles).

**A**nother interesting feature is in the comparison with Multinest, the current version of nested sampling, developed by Farhan Feroz. This is the second time I read a paper involving nested sampling in the past two days. While this PMC implementation does better than nested sampling on the examples processed in the paper, the Multinest outcome remains relevant, particularly because it handles multi-modality fairly well. The authors seem to think parallelisation is an issue with nested sampling, while I do see why: at the most naïve stage, several nested samplers can be run in parallel and the outcomes pulled together.

## AMIS convergence, at last!

Posted in Statistics, University life with tags AMIS, Big'MC, convergence, importance sampling, PMC, seminar, unbiasedness on November 19, 2012 by xi'an**T**his afternoon, Jean-Michel Marin gave his talk at the big’MC seminar. As already posted, it was about a convergence proof for AMIS, which gave me the opportunity to simultaneously read the paper and listen to the author. The core idea for adapting AMIS towards a manageable version is to update the proposal parameter based on the current sample rather than on the whole past. This facilitates the task of establishing convergence to the optimal (pseudo-true) value of the parameter, under an assumption that the optimal value is a know moment of the target. From there, convergence of the weighted mean is somehow natural when the number of simulations grows to infinity. (Note the special asymptotics of AMIS, though, which are that the number of steps goes to infinity while the number of simulations per step grows a wee faster than linearly. In this respect, it is the opposite of PMC, where convergence is of a more traditional nature, pushing the number of simulations per step to infinity.) The second part of the convergence proof is more intricate, as it establishes that the multiple mixture estimator based on the “forward-backward” reweighting of all simulations since step zero does converge to the proper posterior moment. This relies on rather complex assumptions, but remains a magnificent *tour de force*. During the talk, I wondered if, given the Markovian nature of the algorithm (since reweighting only occurs once simulation is over), an alternative estimator based on the optimal value of the simulation parameter would not be better than the original multiple mixture estimator: the proof is based on the equivalence between both versions….