Search Results for author: Jonathan Weare

Found 5 papers, 3 papers with code

Understanding and eliminating spurious modes in variational Monte Carlo using collective variables

no code implementations11 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.

Variational Monte Carlo

Learning forecasts of rare stratospheric transitions from short simulations

no code implementations15 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

A metric on directed graphs and Markov chains based on hitting probabilities

1 code implementation25 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.

Dimensionality Reduction Link Prediction

Umbrella sampling: a powerful method to sample tails of distributions

1 code implementation13 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

Ensemble preconditioning for Markov chain Monte Carlo simulation

1 code implementation13 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.

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