17 citations · 29 across the 22 of their papers we have counts for
5 papers · 1 filter
Replica Conditional Sequential Monte Carlo
Alexander Y. Shestopaloff, Arnaud Doucet
We propose a Markov chain Monte Carlo (MCMC) scheme to perform state inference in non-linear non-Gaussian state-space models. Current state-of-the-art methods to address this probl…
Sampling latent states for high-dimensional non-linear state space models with the embedded HMM method
Alexander Y. Shestopaloff, Radford M. Neal
We propose a new scheme for selecting pool states for the embedded Hidden Markov Model (HMM) Markov Chain Monte Carlo (MCMC) method. This new scheme allows the embedded HMM method…
Efficient Bayesian inference for stochastic volatility models with ensemble MCMC methods
Alexander Y. Shestopaloff, Radford M. Neal
In this paper, we introduce efficient ensemble Markov Chain Monte Carlo (MCMC) sampling methods for Bayesian computations in the univariate stochastic volatility model. We compare…
On Bayesian inference for the M/G/1 queue with efficient MCMC sampling
Alexander Y. Shestopaloff, Radford M. Neal
We introduce an efficient MCMC sampling scheme to perform Bayesian inference in the M/G/1 queueing model given only observations of interdeparture times. Our MCMC scheme uses a com…
MCMC for non-linear state space models using ensembles of latent sequences
Alexander Y. Shestopaloff, Radford M. Neal
Non-linear state space models are a widely-used class of models for biological, economic, and physical processes. Fitting these models to observed data is a difficult inference pro…