Approximation methods for piecewise deterministic Markov processes and their costs
نویسندگان
چکیده
منابع مشابه
Numerical methods for optimal control of piecewise deterministic Markov processes
Scientific Research context: In 1980, M.H.A. Davis [1] introduced in probability theory Piecewise Deterministic Markov Processes (PDMP) as a general class of models suitable for formulating optimization problems in queuing and inventory systems, maintenance-replacement models, investment scheduling and many other areas of operation research. In the continuous-time context, stochastic control th...
متن کاملDemographic noise and piecewise deterministic Markov processes.
We explore a class of hybrid (piecewise deterministic) systems characterized by a large number of individuals inhabiting an environment whose state is described by a set of continuous variables. We use analytical and numerical methods from nonequilibrium statistical mechanics to study the influence that intrinsic noise has on the qualitative behavior of the system. We discuss the application of...
متن کاملPiecewise deterministic Markov processes in biological models
We present a short introduction into the framework of piecewise deterministic Markov processes. We illustrate the abstract mathematical setting with a series of examples related to dispersal of biological systems, cell cycle models, gene expression, physiologically structured populations, as well as neural activity. General results concerning asymptotic properties of stochastic semigroups induc...
متن کاملPiecewise-deterministic Markov Processes for Sequential Monte Carlo and MCMC∗
This talk will introduce piecewise-deterministic Markov processes, and show how they can be used to develop novel, continuous-time, variants of MCMC or SMC. A particular motivation for this work is to develop Monte Carlo methods that can sample from a posterior and that scale well to large-data.
متن کاملPiecewise Deterministic Markov Processes for Continuous-Time Monte Carlo
Recently there have been conceptually new developments in Monte Carlo methods through the introduction of new MCMC and sequential Monte Carlo (SMC) algorithms which are based on continuous-time, rather than discrete-time, Markov processes. This has led to some fundamentally new Monte Carlo algorithms which can be used to sample from, say, a posterior distribution. Interestingly, continuous-time...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Scandinavian Actuarial Journal
سال: 2019
ISSN: 0346-1238,1651-2030
DOI: 10.1080/03461238.2018.1560357