stack overflow on strike

fusing simulation with data science [18-19 July 2023]

In collaboration with the Met Office, my friend and Warwick colleague Rito Dutta is co-organising a two-day workshop in Warwick in July on the use of statistics and machine learning tools in weather prediction. Attendance is free, but registration needed for tea breaks.

van Dantzig seminar

optimal importance sampling

In Stein Π-Importance Sampling, Congye Wang et al. (mostly from Newcastle, UK) build an MCMC scheme with invariant distribution Π targeting a distribution P, showing that the optimal solution (in terms of a discrepancy) differs from P when the chain is Stein-sampled, e..g. via kernel discrepancies. In terms of densities, the solution is

the correction involving the root of a Stein kernel, introduced by Oates, Girolami, and Chopin in their 2017 Series B Read Paper. This is rather paradoxical, even though the outcome does depend on the divergence criterion. Most intriguing!!!

Natural nested sampling

“The nested sampling algorithm solves otherwise challenging, high-dimensional integrals by evolving a collection of live points through parameter space. The algorithm was immediately adopted in cosmology because it partially overcomes three of the major difficulties in Markov chain Monte Carlo, the algorithm traditionally used for Bayesian computation. Nested sampling simultaneously returns results for model comparison and parameter inference; successfully solves multimodal problems; and is naturally self-tuning, allowing its immediate application to new challenges.”

I came across a review on nested sampling in Nature Reviews Methods Primers of May 2022, with a large number of contributing authors, some of whom I knew from earlier papers in astrostatistics. As illustrated by the above quote from the introduction, the tone is definitely optimistic about the capacities of the method, reproducing the original argument that the evidence is the posterior expectation of the likelihood L(θ) under the prior. Which representation, while valid, is not translating into a dimension-free methodology since parameters θ still need be simulated.

“Nested sampling lies in a class of algorithms that form a path of bridging distributions and evolves samples along that path. Nested sampling stands out because the path is automatic and smooth — compression along log X by, on average, 1/𝑛at each iteration — and because along the path is compressed through constrained priors, rather than from the prior to the posterior. This was a motivation for nested sampling as it avoids phase transitions — abrupt changes in the bridging distributions — that cause problems for other methods, including path samplers, such as annealing.”

The elephant in the room is eventually processed, namely the simulation from the prior constrained to the likelihood level sets that in my experience (with, e.g., mixture posteriors) proves most time consuming. This stems from the fact that these level sets are notoriously difficult to evaluate from a given sample: all points stand within the set but they hardly provide any indication of the boundaries of saif set… Region sampling requires to construct a region that bounds the likelihood level set, which requires some knowledge of the likelihood variations to have a chance to remain efficient, incl. in cosmological applications, while regular MCMC steps require an increasing number of steps as the constraint gets tighter and tighter. For otherwise it essentially amounts to duplicating a live particle.