We developed and validated digital twins (DTs) for contrast sensitivity function (CSF) across
12 prediction tasks using a data-driven, generative model approach based on a hierarchical …

Multiple-try Metropolis (MTM) is a popular Markov chain Monte Carlo method with the
appealing feature of being amenable to parallel computing. At each iteration, it samples …

We propose a novel hybrid quantum computing strategy for parallel MCMC algorithms that
generate multiple proposals at each step. This strategy makes the rate-limiting step within …

We present the simplicial sampler, a class of parallel MCMC methods that generate and
choose from multiple proposals at each iteration. The algorithm's multiproposal randomly …

The rise of distributed energy resources (DERs) in power systems demands efficient models
for power flow analysis. Existing models often face challenges in balancing computation …

We develop and implement a Bayesian approach for the estimation of the shape of a two
dimensional annular domain enclosing a Stokes flow from sparse and noisy observations of …

Pathogen genomic data is increasingly being used to investigate transmission dynamics in
infectious disease outbreaks. Combining genomic data with epidemiological data should …

Our proposed algorithm is a prefetching-based multi-proposal Markov Chain Monte Carlo
(PMP-MCMC) method that efficiently explores the target distribution by combining multiple …

This is a concise mathematical introduction to Monte Carlo methods, a rich family of
algorithms with far-reaching applications in science and engineering. Monte Carlo methods …

Sacred and Profane: from the Involutive Theory of MCMC to Helpful Hamiltonian Hacks
arXiv:2410.17398v1 [stat.CO] 22 Oct 2024 Page 1 Sacred and Profane: from the Involutive …