P
Patrick R. Conrad
Researcher at Massachusetts Institute of Technology
Publications - 20
Citations - 681
Patrick R. Conrad is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Markov chain Monte Carlo & Ergodicity. The author has an hindex of 13, co-authored 20 publications receiving 587 citations. Previous affiliations of Patrick R. Conrad include University of Warwick.
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Adaptive Smolyak Pseudospectral Approximations
TL;DR: This paper describes an adaptive method for non-intrusive pseudospectral approximation, based on Smolyak's algorithm with generalized sparse grids, and introduces a greedy heuristic for adaptive refinement of the pseudospectsral approximation.
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Accelerating Asymptotically Exact MCMC for Computationally Intensive Models via Local Approximations
TL;DR: The ergodicity of the approximate Markov chain is proved, showing that it samples asymptotically from the exact posterior distribution of interest, and variations of the algorithm that employ either local polynomial approximations or local Gaussian process regressors are described.
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Statistical analysis of differential equations: introducing probability measures on numerical solutions
TL;DR: In this article, a formal quantification of uncertainty induced by numerical solutions of ordinary and partial differential equation models is presented, where a wide variety of existing solvers can be randomised, inducing a probability measure over the solutions of such differential equations.
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Drake: an efficient executive for temporal plans with choice
TL;DR: Drake is designed to leverage the low latency made possible by a preprocessing step called compilation, while avoiding high memory costs through a compact representation, and to concisely record the implications of the discrete choices, exploiting the structure of the plan to avoid redundant reasoning or storage.
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A priori testing of sparse adaptive polynomial chaos expansions using an ocean general circulation model database
Justin Winokur,Patrick R. Conrad,Ihab Sraj,Omar M. Knio,Ashwanth Srinivasan,W. Carlisle Thacker,Youssef M. Marzouk,Mohamed Iskandarani +7 more
TL;DR: The a priori tests demonstrate that sparse and adaptive pseudo-spectral constructions lead to substantial savings over isotropic sparse sampling in the present setting.