## Arianna Rosenbluth’s hit

Posted in Statistics with tags , , , , , , , , , , on February 8, 2021 by xi'an

## averaged acceptance ratios

Posted in Statistics with tags , , , , , , , , , , , , , on January 15, 2021 by xi'an

In another recent arXival, Christophe Andrieu, Sinan Yıldırım, Arnaud Doucet, and Nicolas Chopin study the impact of averaging estimators of acceptance ratios in Metropolis-Hastings algorithms. (It is connected with the earlier arXival rephrasing Metropolis-Hastings in terms of involutions discussed here.)

“… it is possible to improve performance of this algorithm by using a modification where the acceptance ratio r(ξ) is integrated with respect to a subset of the proposed variables.”

This interpretation of the current proposal makes it a form of Rao-Blackwellisation, explicitly mentioned on p.18, where, using a mixture proposal, with an adapted acceptance probability, it depends on the integrated acceptance ratio only. Somewhat magically using this ratio and its inverse with probability ½. And it increases the average Metropolis-Hastings acceptance probability (albeit with a larger number of simulations). Since the ideal averaging is rarely available, the authors implement a Monte Carlo averaging version. With applications to the exchange algorithm and to reversible jump MCMC. The major application is to pseudo-marginal settings with a high complexity (in the number T of terms) and where the authors’ approach does scale efficiently with T. There is even an ABC side to the story as one illustration is made of the ABC approximation to the posterior of an α-stable sample. As an encompassing proposal for handling Metropolis-Hastings environments with latent variables and several versions of the acceptance ratios, this is quite an interesting paper that I think we will study in further detail with our students.

## general perspective on the Metropolis–Hastings kernel

Posted in Books, Statistics with tags , , , , , , , , , , , , , on January 14, 2021 by xi'an

[My Bristol friends and co-authors] Christophe Andrieu, and Anthony Lee, along with Sam Livingstone arXived a massive paper on 01 January on the Metropolis-Hastings kernel.

“Our aim is to develop a framework making establishing correctness of complex Markov chain Monte Carlo kernels a purely mechanical or algebraic exercise, while making communication of ideas simpler and unambiguous by allowing a stronger focus on essential features (…) This framework can also be used to validate kernels that do not satisfy detailed balance, i.e. which are not reversible, but a modified version thereof.”

A central notion in this highly general framework is, extending Tierney (1998), to see an MCMC kernel as a triplet involving a probability measure μ (on an extended space), an involution transform φ generalising the proposal step (i.e. þ²=id), and an associated acceptance probability ð. Then μ-reversibility occurs for

$\eth(\xi)\mu(\text{d}\xi)= \eth(\phi(\xi))\mu^{\phi}(\text{d}\xi)$

with the rhs involving the push-forward measure induced by μ and φ. And furthermore there is always a choice of an acceptance probability ð ensuring for this equality to happen. Interestingly, the new framework allows for mostly seamless handling of more complex versions of MCMC such as reversible jump and parallel tempering. But also non-reversible kernels, incl. for instance delayed rejection. And HMC, incl. NUTS. And pseudo-marginal, multiple-try, PDMPs, &c., &c. it is remarkable to see such a general theory emerging a this (late?) stage of the evolution of the field (and I will need more time and attention to understand its consequences).

## Arianna Rosenbluth (1927-2020)

Posted in Statistics with tags , , , , , on December 30, 2020 by xi'an

## Berni Alder obituary in Nature [and the Metropolis algorithm]

Posted in Books, Statistics, University life with tags , , , , , , , , , , , on December 4, 2020 by xi'an

When reading through the 15 October issue of Nature, I came across an obituary by David Ceperley for Berni Alder (1925-2020). With Thomas Wainwright, Alder invented the technique of molecular dynamics, “silencing criticism that the results were the product of inaccurate computer arithmetic.”

“Berni Alder pioneered computer simulation, in particular of the dynamics of atoms and molecules in condensed matter. To answer fundamental questions, he encouraged the view that computer simulation was a new way of doing science, one that could connect theory with experiment. Alder’s vision transformed the field of statistical mechanics and many other areas of applied science.”

As I was completely unaware of Alder’s contributions to the field, I was most surprised to read the following

“During his PhD, he and the computer scientist Stan Frankel developed an early Monte Carlo algorithm — one in which the spheres are given random displacements — to calculate the properties of the hard-sphere fluid. The advance was scooped by Nicholas Metropolis and his group at the Los Alamos National Laboratory in New Mexico.”

that would imply missing credit is due!, but I could only find the following information on Stan Frankel’s Wikipedia page: Frankel “worked with PhD candidate Berni Alder in 1949–1950 to develop what is now known as Monte Carlo analysis. They used techniques that Enrico Fermi had pioneered in the 1930s. Due to a lack of local computing resources, Frankel travelled to England in 1950 to run Alder’s project on the Manchester Mark 1 computer. Unfortunately, Alder’s thesis advisor [John Kirkwood] was unimpressed, so Alder and Frankel delayed publication of their results until 1955, in the Journal of Chemical Physics. This left the major credit for the technique to a parallel project by a team including Teller and Metropolis who published similar work in the same journal in 1953.” The (short) paper by Alder, Frankel and Lewinson is however totally silent on a potential precursor to the Metropolis et al. algorithm (included in its references)… It also contains a proposal for a completely uniform filling of a box by particles, provided they do not overlap, but the authors had to stop at 98 particles due to its inefficiency.