3 papers
stat.CO2026
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.ME2026
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…
stat.CO2026
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…