J
Jonathan Weare
Researcher at Courant Institute of Mathematical Sciences
Publications - 86
Citations - 4195
Jonathan Weare is an academic researcher from Courant Institute of Mathematical Sciences. The author has contributed to research in topics: Markov chain Monte Carlo & Computer science. The author has an hindex of 20, co-authored 76 publications receiving 3381 citations. Previous affiliations of Jonathan Weare include University of Chicago & University of California, Berkeley.
Papers
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Journal ArticleDOI
Ensemble samplers with affine invariance
Jonathan Goodman,Jonathan Weare +1 more
TL;DR: A family of Markov chain Monte Carlo methods whose performance is unaffected by affine tranformations of space is proposed, and computational tests show that the affine invariant methods can be significantly faster than standard MCMC methods on highly skewed distributions.
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On the statistical equivalence of restrained-ensemble simulations with the maximum entropy method.
Benoît Roux,Jonathan Weare +1 more
TL;DR: It is demonstrated that the statistical distribution produced by restrained-ensemble simulations is formally consistent with the maximum entropy method of Jaynes, which clarifies the underlying conditions under which restrained-ensingmble simulations will yield results that are consistentwith themaximum entropy method.
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The Theory of Ultra-Coarse-Graining. 1. General Principles
James F. Dama,Anton V. Sinitskiy,Martin McCullagh,Jonathan Weare,Benoiît Roux,Aaron R. Dinner,Gregory A. Voth +6 more
TL;DR: Systematic variational UCG methods are presented that are specifically designed to CG entire protein domains and subdomains into single effective CG particles by augmenting existing effective particle CG schemes to allow for discrete state transitions and configuration-dependent resolution.
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Rare Event Simulation of Small Noise Diffusions
TL;DR: In this paper, an important sampling method for certain rare event problems involving small noise diffusions is proposed, where the second-order partial differential equation (PDE) associated with the diffusion is replaced by a Hamilton-Jacobi equation whose solution is computed pointwise on the fly from its variational formulation.
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Galerkin approximation of dynamical quantities using trajectory data
TL;DR: This work presents a general framework for calculating dynamical quantities by approximating boundary value problems using dynamical operators with a Galerkin expansion, and shows that delay embedding can reduce the information lost when projecting the system's dynamics for model construction.