no code implementations • 9 Feb 2024 • Xinzhu Liang, Sanjaya Lohani, Joseph M. Lukens, Brian T. Kirby, Thomas A. Searles, Kody J. H. Law
In the general framework of Bayesian inference, the target distribution can only be evaluated up-to a constant of proportionality.
1 code implementation • 3 Feb 2024 • Shangda Yang, Vitaly Zankin, Maximilian Balandat, Stefan Scherer, Kevin Carlberg, Neil Walton, Kody J. H. Law
We leverage multilevel Monte Carlo (MLMC) to improve the performance of multi-step look-ahead Bayesian optimization (BO) methods that involve nested expectations and maximizations.
no code implementations • 25 Mar 2022 • Michael D. White, Alexander Tarakanov, Christopher P. Race, Philip J. Withers, Kody J. H. Law
The focus is on materials microstructure.
no code implementations • 24 Mar 2022 • Neil K. Chada, Ajay Jasra, Kody J. H. Law, Sumeetpal S. Singh
In this article we consider Bayesian inference associated to deep neural networks (DNNs) and in particular, trace-class neural network (TNN) priors which were proposed by Sell et al. [39].
1 code implementation • 24 Feb 2021 • Kody J. H. Law, Vitaly Zankin
Furthermore, for p unknown covariates the method can be implemented exactly online with a cost of $O(p^3)$ in computation and $O(p^2)$ in memory per iteration -- in other words, the cost per iteration is independent of n, and in principle infinite data can be considered.
1 code implementation • 24 Feb 2021 • Jeremy Heng, Ajay Jasra, Kody J. H. Law, Alexander Tarakanov
In this article, we consider computing expectations w. r. t.
Bayesian Inference Computation Numerical Analysis Numerical Analysis Methodology
1 code implementation • 14 Jan 2021 • Adam Spannaus, Kody J. H. Law, Piotr Luszczek, Farzana Nasrin, Cassie Putman Micucci, Peter K. Liaw, Louis J. Santodonato, David J. Keffer, Vasileios Maroulas
Significant progress in many classes of materials could be made with the availability of experimentally-derived large datasets composed of atomic identities and three-dimensional coordinates.
no code implementations • 15 May 2019 • David E. Bernholdt, Mark R. Cianciosa, Clement Etienam, David L. Green, Kody J. H. Law, J. M. Park
This paper presents a method for solving the supervised learning problem in which the output is highly nonlinear and discontinuous.
no code implementations • 26 Jul 2018 • Neil K. Chada, Jordan Franks, Ajay Jasra, Kody J. H. Law, Matti Vihola
The resulting estimator leads to inference without a bias from the time-discretisation as the number of Markov chain iterations increases.
Bayesian Inference Methodology Probability Computation 65C05 (primary), 60H35, 65C35, 65C40 (secondary)