156 citations · 383 across the 67 of their papers we have counts for
74 papers
Adaptively switching between a particle marginal Metropolis-Hastings and a particle Gibbs kernel in SMC
Imke Botha, Robert Kohn, Leah South +1
Sequential Monte Carlo squared (SMC; Chopin et al., 2012) methods can be used to sample from the exact posterior distribution of intractable likelihood state space models. Thes…
Particle Mean Field Variational Bayes
Minh-Ngoc Tran, Paco Tseng, Robert Kohn
The Mean Field Variational Bayes (MFVB) method is one of the most computationally efficient techniques for Bayesian inference. However, its use has been restricted to models with c…
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…
Structured variational approximations with skew normal decomposable graphical models
Robert Salomone, Xuejun Yu, David J. Nott +1
Although there is much recent work developing flexible variational methods for Bayesian computation, Gaussian approximations with structured covariance matrices are often preferred…
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…
Deep Learning Enhanced Realized GARCH
Chen Liu, Chao Wang, Minh-Ngoc Tran +1
We propose a new approach to volatility modeling by combining deep learning (LSTM) and realized volatility measures. This LSTM-enhanced realized GARCH framework incorporates and di…