reXing the bridge

As I was re-reading Xiao-Li  Meng’s and Wing Hung Wong’s 1996 bridge sampling paper in Statistica Sinica, I realised they were making the link with Geyer’s (1994) mythical tech report, in the sense that the iterative construction of α functions “converges to the `reverse logistic regression’  described in Geyer (1994) for the two-density cases” (p.839). Although they also saw the later as an “iterative” application of Torrie and Valleau’s (1977) “umbrella sampling” estimator. And cited Bennett (1976) in the Journal of Computational Physics [for which Elsevier still asks for $39.95!] as the originator of the formula [check (6)]. And of the optimal solution (check (8)). Bennett (1976) also mentions that the method fares poorly when the targets do not overlap:

“When the two ensembles neither overlap nor satisfy the above smoothness condition, an accurate estimate of the free energy cannot be made without gathering additional MC data from one or more intermediate ensembles”

in which case this sequence of intermediate targets could be constructed and, who knows?!, optimised. (This may be the chain solution discussed in the conclusion of the paper.) Another optimisation not considered in enough detail is the allocation of the computing time to the two densities, maybe using a bandit strategy to avoid estimating the variance of the importance weights first.

computational methods for statistical mechanics [day #4]

My last day at this ICMS workshop on molecular simulation started [with a double loop of Arthur’s Seat thankfully avoiding the heavy rains of the previous night and then] Chris Chipot‘s magistral entry to molecular simulation for proteins with impressive slides and simulation movies, even though I could not follow the details to really understand the simulation challenges therein, just catching a few connections with earlier talks. A typical example of a cross-disciplinary gap, where the other discipline always seems to be stressing the ‘wrong” aspects. Although this is perfectly unrealistic, it would immensely to prepare talks in pairs for such interdisciplinary workshops! Then Gersende Fort presented results about convergence and efficiency for the Wang-Landau algorithm. The idea is to find the optimal rate for updating the weights of the elements of the partition towards reaching the flat histogram in minimal time. Showing massive gains on toy examples. The next talk went back to molecular biology with Jérôme Hénin‘s presentation on improved adaptive biased sampling. With an exciting notion of orthogonality aiming at finding the slowest directions in the target and putting the computational effort. He also discussed the tension between long single simulations and short repeated ones, echoing a long-going debate in the MCMC community. (He also had a slide with a picture of my first 1983 Apple IIe computer!) Then Antonietta Mira gave a broad perspective on delayed rejection and zero variance estimates. With impressive variance reductions (although some physicists then asked for reduction of order 10¹⁰!). Johannes Zimmer gave a beautiful maths talk on the connection between particle and diffusion limits (PDEs) and Wasserstein geometry and large deviations. (I did not get most of the talk, but it was nonetheless beautiful!) Bert Kappen concluded the day (and the workshop for me) by a nice introduction to control theory. Making connection between optimal control and optimal importance sampling. Which made me idly think of the following problem: what if control cannot be completely… controlled and hence involves a stochastic part? Presumably of little interest as the control would then be on the parameters of the distribution of the control.

“The alanine dipeptide is the fruit fly of molecular simulation.”

The example of this alanine dipeptide molecule was so recurrent during the talks that it justified the above quote by Michael Allen. Not that I am more proficient in the point of studying this protein or using it as a benchmark. Or in identifying the specifics of the challenges of molecular dynamics simulation. Not a criticism of the ICMS workshop obviously, but rather of my congenital difficulty with continuous time processes!!! So I do not return from Edinburgh with a new research collaborative project in molecular dynamics (if with more traditional prospects), albeit with the perception that a minimal effort could bring me to breach the vocabulary barrier. And maybe consider ABC ventures in those (new) domains. (Although I fear my talk on ABC did not impact most of the audience!)

computational methods for statistical mechanics [day #3]

The third day [morn] at our ICMS workshop was dedicated to path sampling. And rare events. Much more into [my taste] Monte Carlo territory. The first talk by Rosalind Allen looked at reweighting trajectories that are not in an equilibrium or are missing the Boltzmann [normalizing] constant. Although the derivation against a calibration parameter looked like the primary goal rather than the tool for constant estimation. Again papers in J. Chem. Phys.! And a potential link with ABC raised by Antonietta Mira… Then Jonathan Weare discussed stratification. With a nice trick of expressing the normalising constants of the different terms in the partition as solution(s) of a Markov system

Because the stochastic matrix M is easier (?) to approximate. Valleau’s and Torrie’s umbrella sampling was a constant reference in this morning of talks. Arnaud Guyader’s talk was in the continuation of Toni Lelièvre’s introduction, which helped a lot in my better understanding of the concepts. Rephrasing things in more statistical terms. Like the distinction between equilibrium and paths. Or bias being importance sampling. Frédéric Cérou actually gave a sort of second part to Arnaud’s talk, using importance splitting algorithms. Presenting an algorithm for simulating rare events that sounded like an opposite nested sampling, where the goal is to get down the target, rather than up. Pushing particles away from a current level of the target function with probability ½. Michela Ottobre completed the series with an entry into diffusion limits in the Roberts-Gelman-Gilks spirit when the Markov chain is not yet stationary. In the transient phase thus.

computational methods for statistical mechanics [day #2]

The last “tutorial” talk at this ICMS workshop [“at the interface between mathematical statistics and molecular simulation”] was given by Tony Lelièvre on adaptive bias schemes in Langevin algorithms and on the parallel replica algorithm. This was both very interesting because of the potential for connections with my “brand” of MCMC techniques and rather frustrating as I felt the intuition behind the physical concepts like free energy and metastability was almost within my reach! The most manageable time in Tony’s talk was the illustration of the concepts through a mixture posterior example. Example that I need to (re)read further to grasp the general idea. (And maybe the book on Free Energy Computations Tony wrote with Mathias Rousset et Gabriel Stoltz.) A definitely worthwhile talk that I hope will get posted on line by ICMS. The other talks of the day were mostly of a free energy nature, some using optimised bias in the Langevin diffusion (except for Pierre Jacob who presented his non-negative unbiased estimation impossibility result).

computational methods for statistical mechanics [day #1]

The first talks of the day at this ICMS workshop [“at the interface between mathematical statistics and molecular simulation”] were actually lectures introducing molecular simulation to statisticians by Michael Allen from Warwick and computational statistics to physicists by Omiros Papaspiliopoulos. Allen’s lecture was quite pedagogical, even though I had to quiz wikipedia for physics terms and notions. Like a force being the gradient of a potential function. He gave a physical meaning to Langevin’ equation. As well as references from the Journal of Chemical Physics that were more recent than 1953. He mentioned alternatives to Langevin’s equation too and I idly wondered at the possibility of using those alternatives as other tools for improved MCMC simulation. Although introducing friction may not be the most promising way to speed up the thing… He later introduced what statisticians call Langevin’ algorithm (MALA) as smart Monte Carlo (Rossky et al., …1978!!!). Recovering Hamiltonian and hybrid Monte Carlo algorithms as a fusion of molecular dynamics, Verlet algorithm, and Metropolis acceptance step! As well as reminding us of the physics roots of umbrella sampling and the Wang-Landau algorithm.

Omiros Papaspiliopoulos also gave a very pedagogical entry to the convergence of MCMC samplers which focussed on the L² approach to convergence. This reminded me of the very first papers published on the convergence of the Gibbs sampler, like the 1990 1992 JCGS paper by Schervish and Carlin. Or the 1991 1996 Annals of Statistics by Amit. (Funny that I located both papers much earlier than when they actually appeared!) One surprising fact was that the convergence of all reversible  ergodic kernels is necessarily geometric. There is no classification of kernels in this topology, the only ranking being through the respective spectral gaps. A good refresher for most of the audience, statisticians included.

The following talks of Day 1 were by Christophe Andrieu, who kept with the spirit of a highly pedagogical entry, covering particle filters, SMC, particle Gibbs and pseudo-marginals, and who hit the right tone I think given the heterogeneous audience. And by Ben Leimkuhler about particle simulation for very large molecular structures. Closing the day by focussing on Langevin dynamics. What I understood from the talk was an improved entry into the resolution of some SPDEs. Gaining two orders when compared with Euler-Marayama.  But missed the meaning of the friction coefficient γ converging to infinity in the title…