2020
DOI: 10.1063/1.5144523
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Inferring effective forces for Langevin dynamics using Gaussian processes

Abstract: Effective forces derived from experimental or in silico molecular dynamics time traces are critical in developing reduced and computationally efficient descriptions of otherwise complex dynamical problems. This helps motivate why it is important to develop methods to efficiently learn effective forces from time series data. A number of methods already exist to do this when data are plentiful but otherwise fail for sparse datasets or datasets where some regions of phase space are undersampled. In addition, any … Show more

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Cited by 19 publications
(19 citation statements)
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“…HMJPs, just like HMMs, do not, however, pertain to the analysis of single-photon arrival in the smFRET, 1 , 9 , 42 , 43 and, for that reason, limitations of the HMM are not intrinsic to single-photon-arrival analysis methods. However, because existing smFRET analysis, starting from single photon arrival, operates within a maximum-likelihood paradigm, it can, in principle, be reframed within a Bayesian framework, 67 such as the HMJP. Doing so, provides such methods with the ability to determine full posterior distributions, not just point estimates, around unknown quantities, thereby propagating uncertainty from the measurement into an uncertainty over the parameters of interest.…”
Section: Discussionmentioning
confidence: 99%
“…HMJPs, just like HMMs, do not, however, pertain to the analysis of single-photon arrival in the smFRET, 1 , 9 , 42 , 43 and, for that reason, limitations of the HMM are not intrinsic to single-photon-arrival analysis methods. However, because existing smFRET analysis, starting from single photon arrival, operates within a maximum-likelihood paradigm, it can, in principle, be reframed within a Bayesian framework, 67 such as the HMJP. Doing so, provides such methods with the ability to determine full posterior distributions, not just point estimates, around unknown quantities, thereby propagating uncertainty from the measurement into an uncertainty over the parameters of interest.…”
Section: Discussionmentioning
confidence: 99%
“…BNPs themselves suggest productive paths forward to tentatively formulate inverse strategies for challenging datasets not otherwise amenable to traditional, parametric Bayesian analysis. 105 …”
Section: Discussionmentioning
confidence: 99%
“…[7][8][9][10][11][12][13] ), but the challenge, then, is to choose the right dynamical model out of the multitude of possibilities 5 . Data-driven Bayesian inference models of single-molecule time series have enjoyed considerable success in recent years [14][15][16][17][18][19] , but they usually require physical insight in order to constrain the space of possible models, and they, too, often assume that the observed dynamics is a one-dimensional random walk even if the number of discrete states is not specified a priori.…”
Section: Introductionmentioning
confidence: 99%