**W**ith Grégoire Clarté, Robin Ryder and Julien Stoehr, all from Paris-Dauphine, we have just arXived a paper on the specifics of ABC-Gibbs, which is a version of ABC where the generic ABC accept-reject step is replaced by a sequence of n conditional ABC accept-reject steps, each aiming at an ABC version of a conditional distribution extracted from the joint and intractable target. Hence an ABC version of the standard Gibbs sampler. What makes it so special is that each conditional can (and should) be conditioning on a different statistic in order to decrease the dimension of this statistic, ideally down to the dimension of the corresponding component of the parameter. This successfully bypasses the curse of dimensionality but immediately meets with two difficulties. The first one is that the resulting sequence of conditionals is not coherent, since it is not a Gibbs sampler on the ABC target. The conditionals are thus incompatible and therefore convergence of the associated Markov chain becomes an issue. We produce sufficient conditions for the Gibbs sampler to converge to a stationary distribution using incompatible conditionals. The second problem is then that, provided it exists, the limiting and also intractable distribution does not enjoy a Bayesian interpretation, hence may fail to be justified from an inferential viewpoint. We however succeed in producing a version of ABC-Gibbs in a hierarchical model where the limiting distribution can be explicited and even better can be weighted towards recovering the original target. (At least with limiting zero tolerance.)

## Archive for convergence

## ABC with Gibbs steps

Posted in Statistics with tags ABC, ABC-Gibbs, Approximate Bayesian computation, Bayesian inference, bois de Boulogne, compatible conditional distributions, contraction, convergence, ergodicity, France, Gibbs sampler, hierarchical Bayesian modelling, incompatible conditionals, La Défense, Paris, stationarity, tolerance, Université Paris Dauphine on June 3, 2019 by xi'an## efficient adaptive importance sampling

Posted in Books, Statistics with tags AMIS, convergence, generalised moments, importance sampling, Kullback-Leibler divergence, martingales, oracle inequalities, population Monte Carlo on June 22, 2018 by xi'an**B**ernard Delyon and François Portier just recently arXived a paper on population or evolutionary importance sampling, pointed out to me by Víctor Elvira. Changing the proposal or importance sampler at each iteration. And averaging the estimates across iterations, but also mentioning AMIS. While drawing a distinction that I do not understand, since the simulation cost remains the same, while improving the variance of the resulting estimator. (But the paper points out later that their martingale technique of proof does not apply in this AMIS case.) Some interesting features of the paper are that

- convergence occurs when the total number of simulations grows to infinity, which is the most reasonable scale for assessing the worth of the method;
- some optimality in the oracle sense is established for the method;
- an improvement is found by eliminating outliers and favouring update rate over simulation rate (at a constant cost). Unsurprisingly, the optimal weight of the t-th estimator is given by its inverse variance (with eqn (13) missing an inversion step). Although it relies on the normalised versions of the target and proposal densities, since it assumes the expectation of the ratio is equal to one.

When updating the proposal or importance distribution, the authors consider a parametric family with the update in the parameter being driven by moment or generalised moment matching, or Kullback reduction as in our population Monte Carlo paper. The interesting technical aspects of the paper include the use of martingale and empirical risk arguments. All in all, quite a pleasant surprise to see some follow-up to our work on that topic, more than 10 years later.

## adaptive independent Metropolis-Hastings

Posted in Statistics with tags adaptive MCMC, convergence, Doeblin's condition, independent Metropolis-Hastings algorithm, Markov chains, MCMC algorithms, Wolfgang Doeblin on May 8, 2018 by xi'an**W**hen rereading this paper by Halden et al. (2009), I was reminded of the earlier and somewhat under-appreciated Gåsemyr (2003). But I find the convergence results therein rather counter-intuitive in that they seem to justify adaptive independent proposals with no strong requirement. Besides the massive Doeblin condition:

“The Doeblin condition essentially requires that all the proposal distribution [sic] has uniformly heavier tails than the target distribution.”

Even when the adaptation is based on an history vector made of rejected values and non-replicated accepted values. Actually convergence of this sequence of adaptive proposals kernels is established under a concentration of the Doeblin constants a¹,a²,… towards one, in the sense that

**E**[(1-a¹)(1-a²)…]=0.

The reason may be that, with chains satisfying a Doeblin condition, there is a probability to reach stationarity at each step. Equal to a¹, a², … And hence to ignore adaptivity since each kernel keep the target π invariant. So in the end this is not so astounding. (The paper also reminded me of Wolfgang [or Vincent] Doeblin‘s short and tragic life.)

## exams

Posted in Kids, Statistics, University life with tags Basu's theorem, bootstrap, convergence, copies, correction, exam, mathematical statistics, Université Paris Dauphine on February 7, 2018 by xi'an**A**s in every term, here comes the painful week of grading hundreds of exams! My mathematical statistics exam was highly traditional and did not even involve Bayesian material, as the few students who attended the lectures were so eager to discuss sufficiency and ancilarity, that I decided to spend an extra lecture on these notions rather than rushing though conjugate priors. Highly traditional indeed with an inverse Gaussian model and a few basic consequences of Basu’s theorem. actually exposed during this lecture. Plus mostly standard multiple choices about maximum likelihood estimation and R programming… Among the major trends this year, I spotted out the widespread use of strange derivatives of negative powers, the simultaneous derivation of two incompatible convergent estimates, the common mixup between the inverse of a sum and the sum of the inverses, the inability to produce the MLE of a constant transform of the parameter, the choice of estimators depending on the parameter, and a lack of concern for Fisher informations equal to zero.

## ABC’ory in Banff [17w5025]

Posted in Mountains, pictures, Statistics, Travel, University life with tags 17w5025, ABC, Approximate Bayesian computation, Banff, BIRS, Canada, convergence, Les Diablerets, Rocky Mountains, synthetic likelihood on February 21, 2017 by xi'an**T**he ABC workshop I co-organised has now started and, despite a few last minutes cancellations, we have gathered a great crowd of researchers on the validation and expansion of ABC methods. Or ABC’ory to keep up with my naming of workshops. The videos of the talks should come up progressively on the BIRS webpage. When I did not forget to launch the recording. The program is quite open and with this size of workshop allows for talks and discussions to last longer than planned: the first days contain several expository talks on ABC convergence, auxiliary or synthetic models, summary constructions, challenging applications, dynamic models, and model assessment. Plus prepared discussions on those topics that hopefully involve several workshop participants. We had also set some time for snap-talks, to induce everyone to give a quick presentation of one’s on-going research and open problems. The first day was rather full but saw a lot of interactions and discussions during and around the talks, a mood I hope will last till Friday! Today in replacement of Richard Everitt who alas got sick just before the workshop, we are conducting a discussion on dimensional issues, part of which is made of parts of the following slides (mostly recycled from earlier talks, including the mini-course in Les Diablerets):

## asymptotic properties of Approximate Bayesian Computation

Posted in pictures, Statistics, Travel, University life with tags ABC, asymptotic normality, Australia, Bayesian inference, concentration inequalities, consistency, convergence, identifiability, Melbourne, Monash University, summary statistics on July 26, 2016 by xi'an**W**ith David Frazier and Gael Martin from Monash University, and with Judith Rousseau (Paris-Dauphine), we have now completed and arXived a paper entitled *Asymptotic Properties of Approximate Bayesian Computation*. This paper undertakes a fairly complete study of the large sample properties of ABC under weak regularity conditions. We produce therein sufficient conditions for posterior concentration, asymptotic normality of the ABC posterior estimate, and asymptotic normality of the ABC posterior mean. Moreover, those (theoretical) results are of significant import for practitioners of ABC as they pertain to the choice of tolerance ε used within ABC for selecting parameter draws. In particular, they [the results] contradict the conventional ABC wisdom that this tolerance should always be taken as *small* as the computing budget allows.

Now, this paper bears some similarities with our earlier paper on the consistency of ABC, written with David and Gael. As it happens, the paper was rejected after submission and I then discussed it in an internal seminar in Paris-Dauphine, with Judith taking part in the discussion and quickly suggesting some alternative approach that is now central to the current paper. The previous version analysed Bayesian consistency of ABC under specific uniformity conditions on the summary statistics used within ABC. But conditions for consistency are now much weaker conditions than earlier, thanks to Judith’s input!

There are also similarities with Li and Fearnhead (2015). Previously discussed here. However, while similar in spirit, the results contained in the two papers strongly differ on several fronts:

- Li and Fearnhead (2015) considers an ABC algorithm based on kernel smoothing, whereas our interest is the original ABC accept-reject and its many derivatives
- our theoretical approach permits a complete study of the asymptotic properties of ABC, posterior concentration, asymptotic normality of ABC posteriors, and asymptotic normality of the ABC posterior mean, whereas Li and Fearnhead (2015) is only concerned with asymptotic normality of the ABC posterior mean estimator (and various related point estimators);
- the results of Li and Fearnhead (2015) are derived under very strict uniformity and continuity/differentiability conditions, which bear a strong resemblance to those conditions in Yuan and Clark (2004) and Creel et al. (2015), while the result herein do not rely on such conditions and only assume very weak regularity conditions on the summaries statistics themselves; this difference allows us to characterise the behaviour of ABC in situations not covered by the approach taken in Li and Fearnhead (2015);

## Bayesian Indirect Inference and the ABC of GMM

Posted in Books, Statistics, University life with tags ABC, ABC-PMC, consistency, convergence, generalised method of moments, importance sampling, indirect inference, kernel density estimator, likelihood-free methods, local regression, noisy ABC on February 17, 2016 by xi'an

“The practicality of estimation of a complex model using ABC is illustrated by the fact that we have been able to perform 2000 Monte Carlo replications of estimation of this simple DSGE model, using a single 32 core computer, in less than 72 hours.” (p.15)

**E**arlier this week, Michael Creel and his coauthors arXived a long paper with the above title, where ABC relates to approximate Bayesian computation. In short, this paper provides deeper theoretical foundations for the local regression post-processing of Mark Beaumont and his coauthors (2002). And some natural extensions. But apparently considering one *univariate* transform η(θ) of interest at a time. The theoretical validation of the method is that the resulting estimators converge at speed √n under some regularity assumptions. Including the identifiability of the parameter θ in the mean of the summary statistics T, which relates to our consistency result for ABC model choice. And a CLT on an available (?) preliminary estimator of η(θ).

The paper also includes a GMM version of ABC which appeal is less clear to me as it seems to rely on a preliminary estimator of the univariate transform of interest η(θ). Which is then randomized by a normal random walk. While this sounds a wee bit like noisy ABC, it differs from this generic approach as the model is not assumed to be known, but rather available through an asymptotic Gaussian approximation. (When the preliminary estimator is available in closed form, I do not see the appeal of adding this superfluous noise. When it is unavailable, it is unclear why a normal perturbation can be produced.)

“[In] the method we study, the estimator is consistent, asymptotically normal, and asymptotically as efficient as a limited information maximum likelihood estimator. It does not require either optimization, or MCMC, or the complex evaluation of the likelihood function.” (p.3)

Overall, I have trouble relating the paper to (my?) regular ABC in that the outcome of the supported procedures is an estimator rather than a posterior distribution. Those estimators are demonstrably endowed with convergence properties, including quantile estimates that can be exploited for credible intervals, but this does not produce a posterior distribution in the ~~classical~~ Bayesian sense. For instance, how can one run model comparison in this framework? Furthermore, each of those inferential steps requires solving another possibly costly optimisation problem.

“Posterior quantiles can also be used to form valid confidence intervals under correct model specification.” (p.4)

Nitpicking(ly), this statement is not correct in that posterior quantiles produce valid credible intervals and only asymptotically correct confidence intervals!

“A remedy is to choose the prior π(θ) iteratively or adaptively as functions of initial estimates of θ, so that the “prior” becomes dependent on the data, which can be denoted as π(θ|T).” (p.6)

This modification of the basic ABC scheme relying on simulation from the prior π(θ) can be found in many earlier references and the iterative construction of a better fitted importance function rather closely resembles ABC-PMC. Once again nitpicking(ly), the importance weights are defined therein (p.6) as the inverse of what they should be.