113 citations · 294 across the 25 of their papers we have counts for
3 papers · 1 filter
Risk and Regret of Hierarchical Bayesian Learners
Jonathan H. Huggins, Joshua B. Tenenbaum
Common statistical practice has shown that the full power of Bayesian methods is not realized until hierarchical priors are used, as these allow for greater "robustness" and the ab…
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…
JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes
Jonathan H. Huggins, Karthik Narasimhan, Ardavan Saeedi +1
Markov jump processes (MJPs) are used to model a wide range of phenomena from disease progression to RNA path folding. However, maximum likelihood estimation of parametric models l…