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stat.CO2025
Smoothing Out Sticking Points: Sampling from Discrete-Continuous Mixtures with Dynamical Monte Carlo by Mapping Discrete Mass into a Latent Universe
Andrew Chin, Akihiko Nishimura
Combining a continuous "slab" density with discrete "spike" mass at zero, spike-and-slab priors provide important tools for inducing sparsity and carrying out variable selection in…
stat.CO2024
MCMC using Hamiltonian dynamics: A unifying framework for Hamiltonian Monte Carlo and piecewise deterministic Markov process samplers
Andrew Chin, Akihiko Nishimura
Piecewise-deterministic Markov process (PDMP) samplers constitute a state-of-the-art Markov chain Monte Carlo paradigm in Bayesian computation, with examples including the zig-zag…
stat.CO2017★ 56 cited
Discontinuous Hamiltonian Monte Carlo for discrete parameters and discontinuous likelihoods
Akihiko Nishimura, David Dunson, Jianfeng Lu
Hamiltonian Monte Carlo has emerged as a standard tool for posterior computation. In this article, we present an extension that can efficiently explore target distributions with di…