156 citations · 391 across the 43 of their papers we have counts for
26 papers · 1 filter
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…
Spectral Subsampling MCMC for Stationary Time Series
Robert Salomone, Matias Quiroz, Robert Kohn +2
Bayesian inference using Markov Chain Monte Carlo (MCMC) on large datasets has developed rapidly in recent years. However, the underlying methods are generally limited to relativel…
Subsampling MCMC - An introduction for the survey statistician
Matias Quiroz, Mattias Villani, Robert Kohn +2
The rapid development of computing power and efficient Markov Chain Monte Carlo (MCMC) simulation algorithms have revolutionized Bayesian statistics, making it a highly practical i…
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…
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…
Fast Inference for Intractable Likelihood Problems using Variational Bayes
David Gunawan, Minh-Ngoc Tran, Robert Kohn
Variational Bayes (VB) is a popular estimation method for Bayesian inference. However, most existing VB algorithms are restricted to cases where the likelihood is tractable, which…