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20112017
most citedPinned Brownian Bridges in the Continuous-Time Limit

1 citations · 2 across the 4 of their papers we have counts for

collaborators

7 papers

cond-mat.stat-mech2017★ 1 cited

Pinned Brownian Bridges in the Continuous-Time Limit

Patrick Malsom, Frank Pinski

The current understanding of pinned Brownian bridges is based on the Onsager-Machlup (OM) functional. The continuous-time limit of the OM functional can be expressed either by usin…

cond-mat.stat-mech2015

Rare Events, the Thermodynamic Action and the Continuous-Time Limit

Patrick Malsom, Frank Pinski

We consider diffusion-like paths that are explored by a particle moving via a conservative force while being in thermal equilibrium with its surroundings. To probe rare transitions…

cond-mat.stat-mech2015

Rare Events, Extremely Rare Events and Fluctuations in a Thermodynamic System

P. J. Malsom, F. J. Pinski

In this paper, we follow in the footsteps of Onsager and Machlup (OM) and consider diffusion-like paths that are explored by a particle moving via a conservative force while being…

math.NA2014★ 1 cited

Algorithms for Kullback-Leibler Approximation of Probability Measures in Infinite Dimensions

Frank J. Pinski, Gideon Simpson, Andrew M. Stuart +1

In this paper we study algorithms to find a Gaussian approximation to a target measure defined on a Hilbert space of functions; the target measure itself is defined via its density…

math.PR2013

Kullback-Leibler Approximation for Probability Measures on Infinite Dimensional Spaces

Frank Pinski, Gideon Simpson, Andrew Stuart +1

In a variety of applications it is important to extract information from a probability measure on an infinite dimensional space. Examples include the Bayesian approach to inver…

math.PR2013

A Function Space HMC Algorithm With Second Order Langevin Diffusion Limit

Michela Ottobre, Natesh S. Pillai, Frank J. Pinski +1

We describe a new MCMC method optimized for the sampling of probability measures on Hilbert space which have a density with respect to a Gaussian; such measures arise in the Bayesi…