14 citations · 35 across the 35 of their papers we have counts for
5 papers · 1 filter
New Estimation Approaches for the Hierarchical Linear Ballistic Accumulator Model
David Gunawan, Guy E. Hawkins, Minh-Ngoc Tran +2
The Linear Ballistic Accumulator (Brown & Heathcote, 2008) model is used as a measurement tool to answer questions about applied psychology. The analyses based on this model depend…
Efficient data augmentation for multivariate probit models with panel data: An application to general practitioner decision-making about contraceptives
Vincent Chin, David Gunawan, Denzil G. Fiebig +2
This article considers the problem of estimating a multivariate probit model in a panel data setting with emphasis on sampling a high-dimensional correlation matrix and improving t…
Subsampling Sequential Monte Carlo for Static Bayesian Models
David Gunawan, Khue-Dung Dang, Matias Quiroz +2
We show how to speed up Sequential Monte Carlo (SMC) for Bayesian inference in large data problems by data subsampling. SMC sequentially updates a cloud of particles through a sequ…
Robust Particle Density Tempering for State Space Models
David Gunawan, Robert Kohn, Minh Ngoc Tran
Density tempering (also called density annealing) is a sequential Monte Carlo approach to Bayesian inference for general state models; it is an alternative to Markov chain Monte Ca…
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…