14 citations · 18 across the 8 of their papers we have counts for
5 papers · 1 filter
Variational Bayes Inference for Spatial Error Models with Missing Data
Anjana Wijayawardhana, David Gunawan, Thomas Suesse
The spatial error model (SEM) is a type of simultaneous autoregressive (SAR) model for analysing spatially correlated data. Markov chain Monte Carlo (MCMC) is one of the most widel…
Optimal prediction of positive-valued spatial processes: asymmetric power-divergence loss
Alan R. Pearse, Noel Cressie, David Gunawan
This article studies the use of asymmetric loss functions for the optimal prediction of positive-valued spatial processes. We focus on the family of power-divergence loss functions…
Reliable Bayesian Inference in Misspecified Models
David T. Frazier, Robert Kohn, Christopher Drovandi +1
We provide a general solution to a fundamental open problem in Bayesian inference, namely poor uncertainty quantification, from a frequency standpoint, of Bayesian methods in missp…
Bayesian Inference for Evidence Accumulation Models with Regressors
Viet Hung Dao, David Gunawan, Robert Kohn +3
Evidence accumulation models (EAMs) are an important class of cognitive models used to analyze both response time and response choice data recorded from decision-making tasks. Deve…
Bayesian Inference for State Space Models using Block and Correlated Pseudo Marginal Methods
P. Choppala, D. Gunawan, J. Chen +2
This article addresses the problem of efficient Bayesian inference in dynamic systems using particle methods and makes a number of contributions. First, we develop a correlated pse…