We propose a general framework to simulate stochastic trajectories with arbitrarily long memory dependence and efficiently evaluate large deviation functions associated to time-extensive observables. This extends the 'cloning' procedure of Giardiná et al (2006 Phys. Rev. Lett. 96 120603) to non-Markovian systems. We demonstrate the validity of this method by testing non-Markovian variants of an ion-channel model and the totally asymmetric exclusion process, recovering results obtainable by other means
We prove a large-deviation principle (LDP) for the sample paths of jump Markov processes in the smal...
We are investigating Markov process expectations for large time of the form $\exp(TF(L_T))$, where $...
The non linear filtering problem consists in computing the conditional distributions of a Markov sig...
We propose a general framework to simulate stochastic trajectories with arbitrarily long memory depe...
supported by QMUL Research-IT and funded by EPSRC Grant No. EP/K000128/1.The theory of large deviati...
We introduce a numerical procedure to evaluate directly the probabilities of large deviations of phy...
International audienceWe introduce and test an algorithm that adaptively estimates large deviation f...
We describe a simple form of importance sampling designed to bound and compute large-deviation rate ...
We develop a space-time large-deviation point of view on Gibbs-non-Gibbs transitions in spin systems...
12 pages, 11 figures. Second part of pair of companion papers, following Part I arXiv:1607.04752Inte...
We describe a framework to reduce the computational effort to evaluate large deviation functions of ...
Large deviation functions contain information on the stability and response of systems driven into n...
PhD Theses.In this thesis we study rare events in di erent nonequilibrium stochastic models both i...
Discrete stochastic processes are widespread in natural systems with many applications across physic...
We develop a space-time large-deviation point of view on Gibbs-non-Gibbs transitions in spin systems...
We prove a large-deviation principle (LDP) for the sample paths of jump Markov processes in the smal...
We are investigating Markov process expectations for large time of the form $\exp(TF(L_T))$, where $...
The non linear filtering problem consists in computing the conditional distributions of a Markov sig...
We propose a general framework to simulate stochastic trajectories with arbitrarily long memory depe...
supported by QMUL Research-IT and funded by EPSRC Grant No. EP/K000128/1.The theory of large deviati...
We introduce a numerical procedure to evaluate directly the probabilities of large deviations of phy...
International audienceWe introduce and test an algorithm that adaptively estimates large deviation f...
We describe a simple form of importance sampling designed to bound and compute large-deviation rate ...
We develop a space-time large-deviation point of view on Gibbs-non-Gibbs transitions in spin systems...
12 pages, 11 figures. Second part of pair of companion papers, following Part I arXiv:1607.04752Inte...
We describe a framework to reduce the computational effort to evaluate large deviation functions of ...
Large deviation functions contain information on the stability and response of systems driven into n...
PhD Theses.In this thesis we study rare events in di erent nonequilibrium stochastic models both i...
Discrete stochastic processes are widespread in natural systems with many applications across physic...
We develop a space-time large-deviation point of view on Gibbs-non-Gibbs transitions in spin systems...
We prove a large-deviation principle (LDP) for the sample paths of jump Markov processes in the smal...
We are investigating Markov process expectations for large time of the form $\exp(TF(L_T))$, where $...
The non linear filtering problem consists in computing the conditional distributions of a Markov sig...