13 citations · 45 across the 11 of their papers we have counts for
11 papers
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…
Graph of Graphs: From Nodes to Supernodes in Graphical Models
Maria De Iorio, Willem van den Boom, Alexandros Beskos +2
High-dimensional data analysis typically focuses on low-dimensional structure, often to aid interpretation and computational efficiency. Graphical models provide a powerful methodo…
The Multivariate Bernoulli detector: Change point estimation in discrete survival analysis
Willem van den Boom, Maria De Iorio, Fang Qian +1
Time-to-event data are often recorded on a discrete scale with multiple, competing risks as potential causes for the event. In this context, application of continuous survival anal…
Bayesian clustering of multiple zero-inflated outcomes
Beatrice Franzolini, Andrea Cremaschi, Willem van den Boom +1
Several applications involving counts present a large proportion of zeros (excess-of-zeros data). A popular model for such data is the Hurdle model, which explicitly models the pro…
On a wider class of prior distributions for graphical models
Abhinav Natarajan, Willem van den Boom, Kristoforus Bryant Odang +1
Gaussian graphical models are useful tools for conditional independence structure inference of multivariate random variables. Unfortunately, Bayesian inference of latent graph stru…
Bayesian Learning of Graph Substructures
Willem van den Boom, Maria De Iorio, Alexandros Beskos
Graphical models provide a powerful methodology for learning the conditional independence structure in multivariate data. Inference is often focused on estimating individual edges…
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…
Bayesian inference on the number of recurrent events: A joint model of recurrence and survival
Willem van den Boom, Maria De Iorio, Marta Tallarita
The number of recurrent events before a terminating event is often of interest. For instance, death terminates an individual's process of rehospitalizations and the number of rehos…
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…
Extrema-weighted feature extraction for functional data
Willem van den Boom, Callie Mao, Rebecca A. Schroeder +1
Motivation: Although there is a rich literature on methods for assessing the impact of functional predictors, the focus has been on approaches for dimension reduction that can fail…