Archive for the University life Category

parallelising MCMC algorithms

Posted in Books, Statistics, University life with tags , , , on December 23, 2014 by xi'an

This paper, A general construction for parallelizing Metropolis-Hastings algorithms, written by Ben Calderhead, was first presented at MCMSki last January and has now appeared in PNAS. It is somewhat related to the recycling idea of Tjelmeland (2004, unpublished) and hence to our 1996 Rao-Blackwellisation paper with George. Although there is no recycling herein.

At each iteration of Ben’s algorithm, N proposed values are generated conditional on the “current” value of the Markov chain, which actually consists of (N+1) components and from which one component is drawn at random to serve as a seed for the next proposal distribution and the simulation of N other values. In short, this is a data-augmentation scheme with the index I on the one side and the N modified components on the other side. The neat trick in the proposal [and the reason for the jump in efficiency] is that the stationary distribution of the auxiliary variable can be determined and hence used (N+1) times in updating the vector of (N+1) components. (Note that picking the index at random means computing all (N+1) possible transitions from one component to the N others. Or even all (N+1)! if the proposals differ. Hence a potential increase in the computing cost, even though what costs the most is usually the likelihood computation, dispatched on the parallel processors.) While there are (N+1) terms involved at each step, the genuine Markov chain is truly over a single chain and the N other proposed values are not recycled. Even though they could be [for Monte Carlo integration purposes], as shown e.g. in our paper with Pierre Jacob and Murray Smith. Something that took a few iterations for me to understand is why Ben rephrases the original Metropolis-Hastings algorithm as a finite state space Markov chain on the set of indices {1,…,N+1} (Proposition 1). Conditionally on the values of the (N+1) vector, the stationary of that sub-chain is no longer uniform. Hence, picking (N+1) indices from the stationary helps in selecting the most appropriate images, which explains why the rejection rate decreases.

The paper indeed evaluates the impact of increasing the number of proposals in terms of effective sample size (ESS), acceptance rate, and mean squared jump distance, based two examples. As often in parallel implementations, the paper suggests an “N-fold increase in computational speed” even though this is simply the effect of running the same algorithm on a single processor and on N parallel processors. If the comparison is between a single proposal Metropolis-Hastings algorithm on a single processor and an N-fold proposal on N processors, I would say the latter is slower because of the selection of the index I that forces all pairs of reverse move.  Nonetheless, since this is an almost free bonus resulting from using N processors, when compared with more complex coupled chains, it sounds worth investigating and comparing with those more complex parallel schemes.

Posterior predictive p-values and the convex order

Posted in Books, Statistics, University life with tags , , , , , , , , , on December 22, 2014 by xi'an

Patrick Rubin-Delanchy and Daniel Lawson [of Warhammer fame!] recently arXived a paper we had discussed with Patrick when he visited Andrew and I last summer in Paris. The topic is the evaluation of the posterior predictive probability of a larger discrepancy between data and model

\mathbb{P}\left( f(X|\theta)\ge f(x^\text{obs}|\theta) \,|\,x^\text{obs} \right)

which acts like a Bayesian p-value of sorts. I discussed several times the reservations I have about this notion on this blog… Including running one experiment on the uniformity of the ppp while in Duke last year. One item of those reservations being that it evaluates the posterior probability of an event that does not exist a priori. Which is somewhat connected to the issue of using the data “twice”.

“A posterior predictive p-value has a transparent Bayesian interpretation.”

Another item that was suggested [to me] in the current paper is the difficulty in defining the posterior predictive (pp), for instance by including latent variables

\mathbb{P}\left( f(X,Z|\theta)\ge f(x^\text{obs},Z^\text{obs}|\theta) \,|\,x^\text{obs} \right)\,,

which reminds me of the multiple possible avatars of the BIC criterion. The question addressed by Rubin-Delanchy and Lawson is how far from the uniform distribution stands this pp when the model is correct. The main result of their paper is that any sub-uniform distribution can be expressed as a particular posterior predictive. The authors also exhibit the distribution that achieves the bound produced by Xiao-Li Meng, Namely that

\mathbb{P}(P\le \alpha) \le 2\alpha

where P is the above (top) probability. (Hence it is uniform up to a factor 2!) Obviously, the proximity with the upper bound only occurs in a limited number of cases that do not validate the overall use of the ppp. But this is certainly a nice piece of theoretical work.

anti-séche

Posted in Kids, pictures, University life with tags , , , , on December 21, 2014 by xi'an

memo2

a neat (theoretical) Monte Carlo result

Posted in Books, Statistics, University life with tags , , , , on December 19, 2014 by xi'an

Mark Huber just arXived a short paper where he develops a Monte Carlo approach that bounds the probability of large errors

\mathbb{P}(|\hat\mu_t-\mu|>\epsilon\mu) < 1/\delta

by computing a lower bound on the sample size r and I wondered at the presence of μ in the bound as it indicates the approach is not translation invariant. One reason is that the standard deviation of the simulated random variables is bounded by cμ. Another reason is that Mark uses as its estimator the median

\text{med}(S_1R_1,\ldots,S_tR_t)

where the S’s are partial averages of sufficient length and the R’s are independent uniforms over (1-ε,1+ε): using those uniforms may improve the coverage of given intervals but it also means that the absolute scale of the error is multiplied by the scale of S, namely μ. I first thought that some a posteriori recentering could improve the bound but since this does not impact the variance of the simulated random variables, I doubt it is possible.

Topological sensitivity analysis for systems biology

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

Michael Stumpf sent me Topological sensitivity analysis for systems biology, written by Ann Babtie and Paul Kirk,  en avant-première before it came out in PNAS and I read it during the trip to NIPS in Montréal. (The paper is published in open access, so everyone can read it now!) The topic is quite central to a lot of debates about climate change, economics, ecology, finance, &tc., namely to assess the impact of using the wrong model to draw conclusions and make decisions about a real phenomenon. (Which reminded me of the distinction between mechanical and phenomenological models stressed by Michael Blum in his NIPS talk.) And it is of much interest from a Bayesian point of view since assessing the worth of a model requires modelling the “outside” of a model, using for instance Gaussian processes as in the talk Tony O’Hagan gave in Warwick earlier this term. I would even go as far as saying that the issue of assessing [and compensating for] how wrong a model is, given available data, may be the (single) most under-assessed issue in statistics. We (statisticians) have yet to reach our Boxian era.

In Babtie et al., the space or universe of models is represented by network topologies, each defining the set of “parents” in a semi-Markov representation of the (dynamic) model. At which stage Gaussian processes are also called for help. Alternative models are ranked in terms of fit according to a distance between simulated data from the original model (sounds like a form of ABC?!). Obviously, there is a limitation in the number and variety of models considered this way, I mean there are still assumptions made on the possible models, while this number of models is increasing quickly with the number of nodes. As pointed out in the paper (see, e.g., Fig.4), the method has a parametric bootstrap flavour, to some extent.

What is unclear is how one can conduct Bayesian inference with such a collection of models. Unless all models share the same “real” parameters, which sounds unlikely. The paper mentions using uniform prior on all parameters, but this is difficult to advocate in a general setting. Another point concerns the quantification of how much one can trust a given model, since it does not seem models are penalised by a prior probability. Hence they all are treated identically. This is a limitation of the approach (or an indication that it is only a preliminary step in the evaluation of models) in that some models within a large enough collection will eventually provide an estimate that differs from those produced by the other models. So the assessment may become altogether highly pessimistic for this very reason.

“If our parameters have a real, biophysical interpretation, we therefore need to be very careful not to assert that we know the true values of these quantities in the underlying system, just because–for a given model–we can pin them down with relative certainty.”

In addition to its relevance for moving towards approximate models and approximate inference, and in continuation of yesterday’s theme, the paper calls for nested sampling to generate samples from the posterior(s) and to compute the evidence associated with each model. (I realised I had missed this earlier paper by Michael and co-authors on nested sampling for system biology.) There is no discussion in the paper on why nested sampling was selected, compared with, say, a random walk Metropolis-Hastings algorithm. Unless it is used in a fully automated way,  but the paper is rather terse on that issue… And running either approach on 10⁷ models in comparison sounds like an awful lot of work!!! Using importance [sampling] nested sampling as we proposed with Nicolas Chopin could be a way to speed up this exploration if all parameters are identical between all or most models.

an extension of nested sampling

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

I was reading [in the Paris métro] Hastings-Metropolis algorithm on Markov chains for small-probability estimation, arXived a few weeks ago by François Bachoc, Lionel Lenôtre, and Achref Bachouch, when I came upon their first algorithm that reminded me much of nested sampling: the following was proposed by Guyader et al. in 2011,

To approximate a tail probability P(H(X)>h),

  • start from an iid sample of size N from the reference distribution;
  • at each iteration m, select the point x with the smallest H(x)=ξ and replace it with a new point y simulated under the constraint H(y)≥ξ;
  • stop when all points in the sample are such that H(X)>h;
  • take

\left(1-\dfrac{1}{N}\right)^{m-1}

as the unbiased estimator of P(H(X)>h).

Hence, except for the stopping rule, this is the same implementation as nested sampling. Furthermore, Guyader et al. (2011) also take advantage of the bested sampling fact that, if direct simulation under the constraint H(y)≥ξ is infeasible, simulating via one single step of a Metropolis-Hastings algorithm is as valid as direct simulation. (I could not access the paper, but the reference list of Guyader et al. (2011) includes both original papers by John Skilling, so the connection must be made in the paper.) What I find most interesting in this algorithm is that it even achieves unbiasedness (even in the MCMC case!).

NIPS 2014

Posted in Kids, pictures, Statistics, Travel, University life with tags , , , , , , , , , , , on December 15, 2014 by xi'an

mugSecond and last day of the NIPS workshops! The collection of topics was quite broad and would have made my choosing an ordeal, except that I was invited to give a talk at the probabilistic programming workshop, solving my dilemma… The first talk by Kathleen Fisher was quite enjoyable in that it gave a conceptual discussion of the motivations for probabilistic languages, drawing an analogy with the early days of computer programming that saw a separation between higher level computer languages and machine programming, with a compiler interface. And calling for a similar separation between the models faced by statistical inference and machine-learning and the corresponding code, if I understood her correctly. This was connected with Frank Wood’s talk of the previous day where he illustrated the concept through a generation of computer codes to approximately generate from standard distributions like Normal or Poisson. Approximately as in ABC, which is why the organisers invited me to talk in this session. However, I was a wee bit lost in the following talks and presumably lost part of my audience during my talk, as I realised later to my dismay when someone told me he had not perceived the distinction between the trees in the random forest procedure and the phylogenetic trees in the population genetic application. Still, while it had for me a sort of Twilight Zone feeling of having stepped in another dimension, attending this workshop was an worthwhile experiment as an eye-opener into a highly different albeit connected field, where code and simulator may take the place of a likelihood function… To the point of defining Hamiltonian Monte Carlo directly on the former, as Vikash Mansinghka showed me at the break.

I completed the day with the final talks in the variational inference workshop, if only to get back on firmer ground! Apart from attending my third talk by Vikash in the conference (but on a completely different topic on variational approximations for discrete particle-ar distributions), a talk by Tim Salimans linked MCMC and variational approximations, using MCMC and HMC to derive variational bounds. (He did not expand on the opposite use of variational approximations to build better proposals.) Overall, I found these two days and my first NIPS conference quite exciting, if somewhat overpowering, with a different atmosphere and a different pace compared with (small or large) statistical meetings. (And a staggering gender imbalance!)

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