## Sequentially Constrained Monte Carlo

Posted in Books, Mountains, pictures, Statistics, University life with tags , , , , , , , , , , on November 7, 2014 by xi'an

This newly arXived paper by S. Golchi and D. Campbell from Vancouver (hence the above picture) considers the (quite) interesting problem of simulating from a target distribution defined by a constraint. This is a question that have bothered me for a long while as I could not come up with a satisfactory solution all those years… Namely, when considering a hard constraint on a density, how can we find a sequence of targets that end up with the restricted density? This is of course connected with the zero measure case posted a few months ago. For instance, how do we efficiently simulate a sample from a Student’s t distribution with a fixed sample mean and a fixed sample variance?

“The key component of SMC is the filtering sequence of distributions through which the particles evolve towards the target distribution.” (p.3)

This is indeed the main issue! The paper considers using a sequence of intermediate targets hardening progressively the constraint(s), along with an SMC sampler, but this recommendation remains rather vague and hence I am at loss as to how to make it work when the exact constraint implies a change of measure. The first example is monotone regression where y has mean f(x) and f is monotone. (Everything is unidimensional here.) The sequence is then defined by adding a multiplicative term that is a function of ∂f/∂x, for instance

Φ(τ∂f/∂x),

with τ growing to infinity to make the constraint moving from soft to hard. An interesting introduction, even though the hard constraint does not imply a change of parameter space or of measure. The second example is about estimating the parameters of an ODE, with the constraint being the ODE being satisfied exactly. Again, not exactly what I was looking for. But with an exotic application to deaths from the 1666 Black (Death) plague.

And then the third example is about ABC and the choice of summary statistics! The sequence of constraints is designed to keep observed and simulated summary statistics close enough when the dimension of those summaries increases, which means they are considered simultaneously rather than jointly. (In the sense of Ratmann et al., 2009. That is, with a multidimensional distance.) The model used for the application of the SMC is the dynamic model of Wood (2010, Nature). The outcome of this specific implementation is not that clear compared with alternatives… And again sadly does not deal with the/my zero measure issue.

## Relevant statistics for Bayesian model choice [hot off the press!]

Posted in Books, Statistics, University life with tags , , , , , , on October 30, 2014 by xi'an

Our paper about evaluating statistics used for ABC model choice has just appeared in Series B! It somewhat paradoxical that it comes out just a few days after we submitted our paper on using random forests for Bayesian model choice, thus bypassing the need for selecting those summary statistics by incorporating all statistics available and letting the trees automatically rank those statistics in term of their discriminating power. Nonetheless, this paper remains an exciting piece of work (!) as it addresses the more general and pressing question of the validity of running a Bayesian analysis with only part of the information contained in the data. Quite usefull in my (biased) opinion when considering the emergence of approximate inference already discussed on this ‘Og…

[As a trivial aside, I had first used fresh from the press(es) as the bracketted comment, before I realised the meaning was not necessarily the same in English and in French.]

## reliable ABC model choice via random forests

Posted in pictures, R, Statistics, University life with tags , , , , , , , on October 29, 2014 by xi'an

After a somewhat prolonged labour (!), we have at last completed our paper on ABC model choice with random forests and submitted it to PNAS for possible publication. While the paper is entirely methodological, the primary domain of application of ABC model choice methods remains population genetics and the diffusion of this new methodology to the users is thus more likely via a media like PNAS than via a machine learning or statistics journal.

When compared with our recent update of the arXived paper, there is not much different in contents, as it is mostly an issue of fitting the PNAS publication canons. (Which makes the paper less readable in the posted version [in my opinion!] as it needs to fit the main document within the compulsory six pages, relegated part of the experiments and of the explanations to the Supplementary Information section.)

## insufficient statistics for ABC model choice

Posted in Books, Kids, Statistics, University life with tags , , , , , , , , , , , on October 17, 2014 by xi'an

[Here is a revised version of my comments on the paper by Julien Stoehr, Pierre Pudlo, and Lionel Cucala, now to appear [both paper and comments] in Statistics and Computing special MCMSki 4 issue.]

Approximate Bayesian computation techniques are 2000′s successors of MCMC methods as handling new models where MCMC algorithms are at a loss, in the same way the latter were able in the 1990′s to cover models that regular Monte Carlo approaches could not reach. While they first sounded like “quick-and-dirty” solutions, only to be considered until more elaborate solutions could (not) be found, they have been progressively incorporated within the statistican’s toolbox as a novel form of non-parametric inference handling partly defined models. A statistically relevant feature of those ACB methods is that they require replacing the data with smaller dimension summaries or statistics, because of the complexity of the former. In almost every case when calling ABC is the unique solution, those summaries are not sufficient and the method thus implies a loss of statistical information, at least at a formal level since relying on the raw data is out of question. This forced reduction of statistical information raises many relevant questions, from the choice of summary statistics to the consistency of the ensuing inference.

In this paper of the special MCMSki 4 issue of Statistics and Computing, Stoehr et al. attack the recurrent problem of selecting summary statistics for ABC in a hidden Markov random field, since there is no fixed dimension sufficient statistics in that case. The paper provides a very broad overview of the issues and difficulties related with ABC model choice, which has been the focus of some advanced research only for a few years. Most interestingly, the authors define a novel, local, and somewhat Bayesian misclassification rate, an error that is conditional on the observed value and derived from the ABC reference table. It is the posterior predictive error rate

$\mathbb{P}^{\text{ABC}}(\hat{m}(y^{\text{obs}})\ne m|S(y^{\text{obs}}))$

integrating in both the model index m and the corresponding random variable Y (and the hidden intermediary parameter) given the observation. Or rather given the transform of the observation by the summary statistic S. The authors even go further to define the error rate of a classification rule based on a first (collection of) statistic, conditional on a second (collection of) statistic (see Definition 1). A notion rather delicate to validate on a fully Bayesian basis. And they advocate the substitution of the unreliable (estimates of the) posterior probabilities by this local error rate, estimated by traditional non-parametric kernel methods. Methods that are calibrated by cross-validation. Given a reference summary statistic, this perspective leads (at least in theory) to select the optimal summary statistic as the one leading to the minimal local error rate. Besides its application to hidden Markov random fields, which is of interest per se, this paper thus opens a new vista on calibrating ABC methods and evaluating their true performances conditional on the actual data. (The advocated abandonment of the posterior probabilities could almost justify the denomination of a paradigm shift. This is also the approach advocated in our random forest paper.)

## likelihood-free inference via classification

Posted in Books, Mountains, pictures, Statistics, Travel, University life with tags , , , , , on August 5, 2014 by xi'an

Last week, Michael Gutmann, Ritabrata Dutta, Samuel Kaski, and Jukka Corander posted on arXiv the last version of the paper they had presented at MCMSki 4. As indicated by its (above) title, it suggests implementing ABC based on classification tools. Thus making it somewhat connected to our recent random forest paper.

The starting idea in the paper is that datasets generated from distributions with different parameters should be easier to classify than datasets generated from distributions with the same parameters. And that classification accuracy naturally induces a distance between datasets and between the parameters behind those datasets. We had followed some of the same track when starting using random forests, before realising that for our model choice setting, proceeding the entire ABC way once the random forest procedure had been constructed was counter-productive. Random forests are just too deadly as efficient model choice machines to try to compete with them through an ABC postprocessing. Performances are just… Not. As. Good!

A side question: I have obviously never thought about that before but why is the naïve Bayes classification rule so called?! It never sounded very Bayesian to me to (a) use the true value of the parameter and (b) average the classification performances. Interestingly, the authors (i) show identical performances of other classification methods (Fig. 2) and (ii) an exception for MA time series: when we first experimented random forests, raw data from an MA(2) model was tested to select between MA(1) and  MA(2) models, and the performances of the resulting random forest were quite poor.

Now, an opposition between our two approaches is that Michael and his coauthors also include point estimation within the range of classification-based ABC inference. As we stressed in our paper, we restrict the range to classification and model choice because we do not think those machine learning tools are stable and powerful enough to perform regression and posterior probability approximation. I also see a practical weakness in the estimation scheme proposed in this new paper. Namely that the Monte Carlo gets into the way of the consistency theorem. And possibly of the simulation method itself. Another remark is that, while the authors compare the fit produced by different classification methods, there should be a way to aggregate them towards higher efficiency. Returning once more to our random forest paper, we saw improved performances each time we included a reference method, from LDA to SVMs. It would be interesting to see a (summary) variable selection version of the proposed method. A final remark is that computing time and effort do not seem to get mentioned in the paper (unless Indian jetlag confuses me more than usual). I wonder how fast the computing effort grows with the sample size to reach parametric and quadratic convergence rates.

## ABC [almost] in the front news

Posted in pictures, Statistics, University life with tags , , , , , , , , , , , , , , on July 7, 2014 by xi'an

My friend and Warwick colleague Gareth Roberts just published a paper in Nature with Ellen Brooks-Pollock and Matt Keeling from the University of Warwick on the modelling of bovine tuberculosis dynamics in Britain and on the impact of control measures. The data comes from the Cattle Tracing System and the VetNet national testing database. The mathematical model is based on a stochastic process and its six parameters are estimated by sequential ABC (SMC-ABC). The summary statistics chosen in the model are the number of infected farms per county per year and the number of reactors (cattle failing a test) per county per year.

“Therefore, we predict that control of local badger populations and hence control of environmental transmission will have a relatively limited effect on all measures of bovine TB incidence.”

This advanced modelling of a comprehensive dataset on TB in Britain quickly got into a high profile as it addresses the highly controversial (not to say plain stupid) culling of badgers (who also carry TB) advocated by the government. The study concludes that “only generic measures such as more national testing, whole herd culling or vaccination that affect all routes of transmission are effective at controlling the spread of bovine TB.” While the elimination of badgers from the English countryside would have a limited effect.  Good news for badgers! And the Badger Trust. Unsurprisingly, the study was immediately rejected by the UK farming minister! Not only does he object to the herd culling solution for economic reasons, but he “cannot accept the paper’s findings”. Maybe he does not like ABC… More seriously, the media oversimplified the findings of the study, “as usual”, with e.g. The Guardian headline of “tuberculosis threat requires mass cull of cattle”.

## early rejection MCMC

Posted in Books, Statistics, University life with tags , , , , , , , , on June 16, 2014 by xi'an

In a (relatively) recent Bayesian Analysis paper on efficient MCMC algorithms for climate models, Antti Solonen, Pirkka Ollinaho, Marko Laine, Heikki Haario, Johanna Tamminen and Heikki Järvinen propose an early rejection scheme to speed up Metropolis-Hastings algorithms. The idea is to consider a posterior distribution (proportional to)

$\pi(\theta|y)= \prod_{k=1}^nL_i(\theta|y)$

such that all terms in the product are less than one and to compare the uniform u in the acceptance step of the Metropolis-Hastings algorithm to

$L_1(\theta'|y)/\pi(\theta|y),$

then, if u is smaller than the ratio, to

$L_1(\theta'|y)L_2(\theta'|y)/\pi(\theta|y),$

and so on, until the new value has been rejected or all terms have been evaluated. The scheme obviously stops earlier than the regular Metropolis-Hastings algorithm, at no significant extra cost when the product above does not factor through a sufficient statistic. Solonen et al.  suggest ordering the terms so that the computationally simpler ones are computed first. The upper bound assumption requires and is equivalent to finding the maximum on each term of the product, though, which may be costly in its own for non-standard distributions. With my students Marco Banterle and Clara Grazian, we actually came upon this paper when preparing our delayed acceptance paper as (a) it belongs to the same category of accelerated MCMC methods (delayed acceptance and early rejection are somehow synonymous!) and (b) it mentions the early prefetching papers of Brockwell (2005) and Strid (2009).

“The acceptance probability in ABC is commonly very low, and many proposals are rejected, and ER can potentially help to detect the rejections sooner.”

In the conclusion, Solonen et al. point out a possible link with ABC but, apart from the general idea of rejecting earlier by looking at a subsample or at a proxy simulation of a summary statistics, which is also the idea at the core of Dennis Prangle’s lazy ABC, there is no obvious impact on a likelihood-free method like ABC.