110 citations · 155 across the 15 of their papers we have counts for
11 papers · 1 filter
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…
Wasserstein Gaussianization and Efficient Variational Bayes for Robust Bayesian Synthetic Likelihood
Nhat-Minh Nguyen, Minh-Ngoc Tran, Christopher Drovandi +1
The Bayesian Synthetic Likelihood (BSL) method is a widely-used tool for likelihood-free Bayesian inference. This method assumes that some summary statistics are normally distribut…
Transformations in Semi-Parametric Bayesian Synthetic Likelihood
Jacob W. Priddle, Christopher Drovandi
Bayesian synthetic likelihood (BSL) is a popular method for performing approximate Bayesian inference when the likelihood function is intractable. In synthetic likelihood methods,…
Efficient Bayesian synthetic likelihood with whitening transformations
Jacob W. Priddle, Scott A. Sisson, David T. Frazier +1
Likelihood-free methods are an established approach for performing approximate Bayesian inference for models with intractable likelihood functions. However, they can be computation…
BSL: An R Package for Efficient Parameter Estimation for Simulation-Based Models via Bayesian Synthetic Likelihood
Ziwen An, Leah F South, Christopher Drovandi
Bayesian synthetic likelihood (BSL) is a popular method for estimating the parameter posterior distribution for complex statistical models and stochastic processes that possess a c…
Particle Methods for Stochastic Differential Equation Mixed Effects Models
Imke Botha, Robert Kohn, Christopher Drovandi
Parameter inference for stochastic differential equation mixed effects models (SDEMEMs) is a challenging problem. Analytical solutions for these models are rarely available, which…