1 code implementation • 13 Jul 2016 • Charles Matthews, Jonathan Weare, Benedict Leimkuhler
We describe parallel Markov chain Monte Carlo methods that propagate a collective ensemble of paths, with local covariance information calculated from neighboring replicas.
1 code implementation • 13 Dec 2017 • Charles Matthews, Jonathan Weare, Andrey Kravtsov, Elise Jennings
We present the umbrella sampling (US) technique and show that it can be used to sample extremely low probability areas of the posterior distribution that may be required in statistical analyses of data.
Instrumentation and Methods for Astrophysics
1 code implementation • 25 Jun 2020 • Zachary M. Boyd, Nicolas Fraiman, Jeremy L. Marzuola, Peter J. Mucha, Braxton Osting, Jonathan Weare
The shortest-path, commute time, and diffusion distances on undirected graphs have been widely employed in applications such as dimensionality reduction, link prediction, and trip planning.
no code implementations • 15 Feb 2021 • Justin Finkel, Robert J. Webber, Dorian S. Abbot, Edwin P. Gerber, Jonathan Weare
We compute the probability and lead time efficiently by solving equations involving the transition operator, which encodes all information about the dynamics.
Atmospheric and Oceanic Physics Dynamical Systems Data Analysis, Statistics and Probability
no code implementations • 11 Nov 2022 • huan zhang, Robert J. Webber, Michael Lindsey, Timothy C. Berkelbach, Jonathan Weare
The use of neural network parametrizations to represent the ground state in variational Monte Carlo (VMC) calculations has generated intense interest in recent years.
1 code implementation • 22 Mar 2023 • John Strahan, Spencer C. Guo, Chatipat Lorpaiboon, Aaron R. Dinner, Jonathan Weare
Understanding dynamics in complex systems is challenging because there are many degrees of freedom, and those that are most important for describing events of interest are often not obvious.
no code implementations • 8 Oct 2023 • Atsushi Shimizu, Xiaoou Cheng, Christopher Musco, Jonathan Weare
We show how to obtain improved active learning methods in the agnostic (adversarial noise) setting by combining marginal leverage score sampling with non-independent sampling strategies that promote spatial coverage.
2 code implementations • 12 Apr 2024 • huan zhang, Justin Finkel, Dorian S. Abbot, Edwin P. Gerber, Jonathan Weare
This work demonstrates the potential for machine learning methods to extract meaningful precursors of extreme weather events and achieve better prediction using limited observational data.