13 citations · 45 across the 11 of their papers we have counts for
4 papers · 1 filter
Doubly Adaptive Importance Sampling
Willem van den Boom, Andrea Cremaschi, Alexandre H. Thiery
We propose an adaptive importance sampling scheme for Gaussian approximations of intractable posteriors. Optimization-based approximations like variational inference can be too ina…
The G-Wishart Weighted Proposal Algorithm: Efficient Posterior Computation for Gaussian Graphical Models
Willem van den Boom, Alexandros Beskos, Maria De Iorio
Gaussian graphical models can capture complex dependency structures among variables. For such models, Bayesian inference is attractive as it provides principled ways to incorporate…
Unbiased approximation of posteriors via coupled particle Markov chain Monte Carlo
Willem van den Boom, Ajay Jasra, Maria De Iorio +2
Markov chain Monte Carlo (MCMC) is a powerful methodology for the approximation of posterior distributions. However, the iterative nature of MCMC does not naturally facilitate its…
Approximating posteriors with high-dimensional nuisance parameters via integrated rotated Gaussian approximation
Willem van den Boom, Galen Reeves, David B. Dunson
Posterior computation for high-dimensional data with many parameters can be challenging. This article focuses on a new method for approximating posterior distributions of a low- to…