Estimation and prediction in high dimensional multivariate factor stochastic volatility models
is an important and active research area, because such models allow a parsimonious …

Density tempering (also called density annealing) is a sequential Monte Carlo approach to
Bayesian inference for general state models which is an alternative to Markov chain Monte …

A new approach to state filtering and parameter estimation for a class of stochastic volatility
models for which the likelihood function is unknown is considered. The alpha-stable …

Abstract Particle Markov Chain Monte Carlo (PMCMC) is a powerful approach to Bayesian
computation for general state space models. Our article enables PMCMC to handle a larger …

Particle MCMC involves using a particle filter within an MCMC algorithm. For inference of a
model which involves an unobserved stochastic process, the standard implementation uses …

The Bayesian Mallows model is a flexible tool for analyzing data in the form of complete or
partial rankings, and transitive or intransitive pairwise preferences. In many potential …

Sequential Monte Carlo squared (SMC $^ 2$; Chopin et al., 2012) methods can be used to
sample from the exact posterior distribution of intractable likelihood state space models …

Estimation and prediction in high dimensional multivariate factor stochastic volatility models
is an important and active research area because such models allow a parsimonious …

Calibrating statistical models to data can be a challenging task, particularly when the model
is difficult or time consuming to evaluate. Methods that infer the parameters of these models …

This paper develops a new Bayesian algorithm to efficiently estimate non-linear/non-
Gaussian state space models with abruptly changing parameters. Within the Particle Gibbs …