Archive for the University life Category

PMC for combinatoric spaces

Posted in Statistics, University life with tags , , , , , , , 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 qi,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(qi,t)    (**)

or does (*) apply to every qi,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.

Fairly 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(xi,t)/qi,t(xi,t)}h(qi,t),  xi,t~qi,t(x)

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

  1. associating the qi,t “s with a partition Si,t of the support of p and using instead

    i,t {p(xi,t)/qi,t(xi,t)}h(qi,t), xi,t~qi,t(x)

  2. resorting to AMIS under the assumption (**) and using instead

    1/n ∑i,t {p(xi,t)/∑j,t qj,t(xi,t)}h(qi,t), xi,t~qi,t(x)

but I am open to further suggestions!

Ulam’s grave [STAN post]

Posted in Books, Kids, pictures, Travel, University life with tags , , , , , , , on July 27, 2014 by xi'an

ulamSince Stan Ulam is buried in Cimetière du Montparnasse, next to CREST, Andrew and I paid his grave a visit on a sunny July afternoon. Among elaborate funeral constructions, the Aron family tomb is sober and hidden behind funeral houses. It came as a surprise to me to discover that Ulam had links with France to the point of him and his wife being buried in Ulam’s wife family vault. Since we were there, we took a short stroll to see Henri Poincaré’s tomb in the Poincaré-Boutroux vault (missing Henri’s brother, the French president Raymond Poincaré). It came as a surprise that someone had left a folder with the cover of 17 equations that changed the World on top of the tomb). Even though the book covers Poincaré’s work on the three body problem as part of Newton’s formula. There were other mathematicians in this cemetery, but this was enough necrophiliac tourism for one day.


off to Bangalore

Posted in Statistics, Travel, University life with tags , , , on July 26, 2014 by xi'an

I am off to Bangalore for a few days, taking part in an Indo-French workshop on statistics and mathematical biology run by the Indo-French Centre for Applied Mathematics (IFCAM).

a statistical test for nested sampling

Posted in Books, Statistics, University life with tags , , , , , on July 25, 2014 by xi'an

A new arXival on nested sampling: “A statistical test for nested sampling algorithms” by Johannes Buchner. The point of the test is to check if versions of the nested sampling algorithm that fail to guarantee increased likelihood (or nesting) at each step are not missing parts of the posterior mass. and hence producing biased evidence approximations. This applies to MultiNest for instance. This version of nest sampling evaluates the above-threshold region by drawing hyper-balls around the remaining points. A solution which is known to fail in one specific but meaningful case. Buchner’s  arXived paper proposes an hyper-pyramid distribution for which the volume of any likelihood constrained set is known. Hence allowing for a distribution test like Kolmogorov-Smirnov. Confirming the findings of Beaujean and Caldwell (2013). The author then proposes an alternative to MultiNest that is more robust but also much more costly as it computes distances between all pairs of bootstrapped samples. This solution passes the so-called “shrinkage test”, but it is orders of magnitude less efficient than MultiNest. And also simply shows that its coverage is fine for a specific target rather than all possible targets. I wonder if a solution to the problem is at all possible given that evaluating a support or a convex hull is a complex problem which complexity explodes with the dimension.

ABC in Sydney [guest post #2]

Posted in pictures, Statistics, University life with tags , , , on July 24, 2014 by xi'an

[Here is a second guest post on the ABC in Sydney workshop, written by Chris Drovandi]

First up Dennis Prangle presented his recent work on “Lazy ABC”, which can speed up ABC by potentially abandoning model simulations early that do not look promising. Dennis introduces a continuation probability to ensure that the target distribution of the approach is still the ABC target of interest. In effect, the ABC likelihood is estimated to be 0 if early stopping is performed otherwise the usual ABC likelihood is inflated by dividing by the continuation probability, ensuring an unbiased estimator of the ABC likelihood. The drawback is that the ESS (Dennis uses importance sampling) of the lazy approach will likely be less than usual ABC for a fixed number of simulations; but this should be offset by the reduction in time required to perform said simulations. Dennis also presented some theoretical work for optimally tuning the method, which I need more time to digest.
This was followed by my talk on Bayesian indirect inference methods that use a parametric auxiliary model (a slightly older version here). This paper has just been accepted by Statistical Science.
Morning tea was followed by my PhD student, Brenda Vo, who presented an interesting application of ABC to cell spreading experiments. Here an estimate of the diameter of the cell population was used as a summary statistic. It was noted after Brenda’s talk that this application might be a good candidate for Dennis’ Lazy ABC idea. This talk was followed by a much more theoretical presentation by Pierre del Moral on how particle filter methodologies can be adapted to the ABC setting and also a general framework for particle methods.
Following lunch, Guilherme Rodrigues presented a hierarchical Gaussian Process model for kernel density estimation in the presence of different subgroups. Unfortunately my (lack of) knowledge on non-parametric methods prevents me from making any further comment except that the model looked very interesting and ABC seemed a good candidate for calibrating the model. I look forward to the paper appearing on-line.
The next presentation was by Gael Martin who spoke about her research on using ABC for estimation of complex state space models. This was probably my favourite talk of the day, and not only because it is very close to my research interests. Here the score of the Euler discretised approximation of the generative model was used as summary statistics for ABC. From what I could gather, it was demonstrated that the ABC posterior based on the score or the MLE of the auxiliary model were the same in the limit as ε 0 (unless I have mis-interpreted). This is a very useful result in itself; using the score to avoid an optimisation required for the MLE can save a lot of computation. The improved approximations of the proposed approach compared with the results that use the likelihood of the Euler discretisation were quite promising. I am certainly looking forward to this paper coming out.
Matt Moores drew the short straw and had the final presentation on the Friday afternoon. Matt spoke about this paper (an older version is available here), of which I am now a co-author. Matt’s idea is that doing some pre-simulations across the prior space and determining a mapping between the parameter of interest and the mean and variance of the summary statistic can significantly speed up ABC for the Potts model, and potentially other ABC applications. The results of the pre-computation step are used in the main ABC algorithm, which no longer requires simulation of pseudo-data but rather a summary statistic can be simulated from the fitted auxiliary model in the pre-processing step. Whilst this approach does introduce a couple more layers of approximation, the gain in computation time was up to two orders of magnitude. The talks by Matt, Gael and myself gave a real indirect inference flavour to this year’s ABC in…


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