7 citations · 7 across the 4 of their papers we have counts for
6 papers
The Correlated Particle Hybrid Sampler for State Space Models
David Gunawan, Chris Carter, Robert Kohn
Particle Markov Chain Monte Carlo (PMCMC) is a general computational approach to Bayesian inference for general state space models. Our article scales up PMCMC in terms of the numb…
Efficient Bayesian inference for multivariate factor stochastic volatility models with leverage
David Gunawan, Chris Carter, Robert Kohn
This paper discusses the efficient Bayesian estimation of a multivariate factor stochastic volatility (Factor MSV) model with leverage. We propose a novel approach to construct the…
Efficient Bayesian estimation for flexible panel models for multivariate outcomes: Impact of life events on mental health and excessive alcohol consumption
David Gunawan, Chris carter, Denzil Fiebig +1
The problem we consider considers estimating a multivariate longitudinal panel data model whose outcomes can be a combination of discrete and continuous variables. This problem is…
An extended space approach for particle Markov chain Monte Carlo methods
Christopher K. Carter, Eduardo F. Mendes, Robert Kohn
In this paper we consider fully Bayesian inference in general state space models. Existing particle Markov chain Monte Carlo (MCMC) algorithms use an augmented model that takes int…
A flexible Particle Markov chain Monte Carlo method
Eduardo F. Mendes, Christopher K. Carter, David Gunawan +1
Particle Markov Chain Monte Carlo methods are used to carry out inference in non-linear and non-Gaussian state space models, where the posterior density of the states is approximat…
Bayesian Covariance Matrix Estimation using a Mixture of Decomposable Graphical Models
Helen Armstrong, Christopher K. Carter, Kevin F. Wong +1
A Bayesian approach is used to estimate the covariance matrix of Gaussian data. Ideas from Gaussian graphical models and model selection are used to construct a prior for the covar…