113 citations · 294 across the 25 of their papers we have counts for
4 papers · 1 filter
Practical bounds on the error of Bayesian posterior approximations: A nonasymptotic approach
Jonathan H. Huggins, Trevor Campbell, Mikołaj Kasprzak +1
Bayesian inference typically requires the computation of an approximation to the posterior distribution. An important requirement for an approximate Bayesian inference algorithm is…
Quantifying the accuracy of approximate diffusions and Markov chains
Jonathan H. Huggins, James Zou
Markov chains and diffusion processes are indispensable tools in machine learning and statistics that are used for inference, sampling, and modeling. With the growth of large-scale…
Truncated Random Measures
Trevor Campbell, Jonathan H. Huggins, Jonathan P. How +1
Completely random measures (CRMs) and their normalizations are a rich source of Bayesian nonparametric priors. Examples include the beta, gamma, and Dirichlet processes. In this pa…
Sequential Monte Carlo as Approximate Sampling: bounds, adaptive resampling via -ESS, and an application to Particle Gibbs
Jonathan H. Huggins, Daniel M. Roy
Sequential Monte Carlo (SMC) algorithms were originally designed for estimating intractable conditional expectations within state-space models, but are now routinely used to genera…