156 citations · 391 across the 43 of their papers we have counts for
8 papers · 1 filter
Assessment and adjustment of approximate inference algorithms using the law of total variance
Xuejun Yu, David J. Nott, Minh-Ngoc Tran +1
A common method for assessing validity of Bayesian sampling or approximate inference methods makes use of simulated data replicates for parameters drawn from the prior. Under conti…
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…
Identifying relationships between cognitive processes across tasks, contexts, and time
Laura Wall, David Gunawan, Scott D. Brown +3
It is commonly assumed that a specific testing occasion (task, design, procedure, etc.) provides insights that generalise beyond that occasion. This assumption is infrequently care…
Variational Bayes on Manifolds
Minh-Ngoc Tran, Dang H. Nguyen, Duy Nguyen
Variational Bayes (VB) has become a widely-used tool for Bayesian inference in statistics and machine learning. Nonetheless, the development of the existing VB algorithms is so far…
Time-evolving psychological processes over repeated decisions
David Gunawan, Guy E. Hawkins, Robert Kohn +2
Many psychological experiments have subjects repeat a task to gain the statistical precision required to test quantitative theories of psychological performance. In such experiment…
Robustly estimating the marginal likelihood for cognitive models via importance sampling
Minh-Ngoc Tran, Marcel Scharth, David Gunawan +3
Recent advances in Markov chain Monte Carlo (MCMC) extend the scope of Bayesian inference to models for which the likelihood function is intractable. Although these developments al…