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20072018
most citedAn extended space approach for particle Markov chain Monte Carlo methods

7 citations · 7 across the 4 of their papers we have counts for

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5 papers · 1 filter

stat.ME2018

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…

stat.ME2017

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…

stat.ME2017

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…

stat.ME2014★ 7 cited

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…

stat.ME2007

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…