82 citations · 130 across the 2 of their papers we have counts for
5 papers
Parallel local approximation MCMC for expensive models
Patrick Conrad, Andrew Davis, Youssef Marzouk +2
Performing Bayesian inference via Markov chain Monte Carlo (MCMC) can be exceedingly expensive when posterior evaluations invoke the evaluation of a computationally expensive model…
Probability Measures for Numerical Solutions of Differential Equations
Patrick R. Conrad, Mark Girolami, Simo Särkkä +2
In this paper, we present a formal quantification of epistemic uncertainty induced by numerical solutions of ordinary and partial differential equation models. Numerical solutions…
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…
Drake: An Efficient Executive for Temporal Plans with Choice
Patrick Raymond Conrad, Brian Williams
This work presents Drake, a dynamic executive for temporal plans with choice. Dynamic plan execution strategies allow an autonomous agent to react quickly to unfolding events, impr…
Adaptive Smolyak Pseudospectral Approximations
Patrick R. Conrad, Youssef M. Marzouk
Polynomial approximations of computationally intensive models are central to uncertainty quantification. This paper describes an adaptive method for non-intrusive pseudospectral ap…