56 citations · 56 across the 3 of their papers we have counts for
4 papers
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…
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…
Constructing Genetic Risk Scores: Robust Bayesian Approach through Projected Summary Statistics and Flexible Shrinkage
Yuzheng Dun, Nilanjan Chatterjee, Jin Jin +1
Polygenic risk scores (PRS) developed from genome-wide association studies (GWAS) can be used for risk stratification by quantifying the genetic contribution to disease, and many c…
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…