82 citations · 204 across the 14 of their papers we have counts for
3 papers · 1 filter
Ergodicity of Approximate MCMC Chains with Applications to Large Data Sets
Natesh S. Pillai, Aaron Smith
In many modern applications, difficulty in evaluating the posterior density makes performing even a single MCMC step slow. This difficulty can be caused by intractable likelihood f…
The Use of a Single Pseudo-Sample in Approximate Bayesian Computation
Luke Bornn, Natesh Pillai, Aaron Smith +1
We analyze the computational efficiency of approximate Bayesian computation (ABC), which approximates a likelihood function by drawing pseudo-samples from the associated model. For…
Accelerating Asymptotically Exact MCMC for Computationally Intensive Models via Local Approximations
Patrick R. Conrad, Youssef M. Marzouk, Natesh S. Pillai +1
We construct a new framework for accelerating Markov chain Monte Carlo in posterior sampling problems where standard methods are limited by the computational cost of the likelihood…