Results 11 to 20 of about 1,363 (183)
Communicating Piecewise Deterministic Markov Processes [PDF]
IFAC Proceedings Volumes, 2003 Abstract In this paper we introduce CPDPs (Communicating Piecewise Deterministic Markov Processes) as an automata formalism for compositional specification of hybrid systems of the type PDP. A CPDP can be seen as an automaton representation of a PDP, with the extra possibility of interaction with other processes via a new concept that we call passive
Strubbe, S.N. +2 more
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Piecewise deterministic Markov process — recent results [PDF]
ESAIM: Proceedings, 2014 We give a short overview of recent results on a specific class of Markov process: the Piecewise Deterministic Markov Processes (PDMPs). We first recall the definition of these processes and give some general results. On more specific cases such as the TCP model or a model of switched vector fields, better results can be proved, especially as regards ...
Azaïs, Romain +4 more
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Numerical methods for piecewise deterministic Markov processes with boundary
ESAIM: Proceedings and Surveys, 2014 In this paper is described the general aspect of a numerical method for piecewise deterministic Markov processes with boundary. Under very natural hypotheses, a crucial result about uniqueness of solution of a generalized Kolmogorov ...
Goudenège Ludovic
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Hypocoercivity of piecewise deterministic Markov process-Monte Carlo [PDF]
The Annals of Applied Probability, 2021 In this work, we establish $\mathrm{L}^2$-exponential convergence for a broad class of Piecewise Deterministic Markov Processes recently proposed in the context of Markov Process Monte Carlo methods and covering in particular the Randomized Hamiltonian Monte Carlo, the Zig-Zag process and the Bouncy Particle Sampler. The kernel of the symmetric part of
Andrieu, Christophe +3 more
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Bisimulation for Communicating Piecewise Deterministic Markov Processes (CPDPs) [PDF]
, 2005 CPDPs (Communicating Piecewise Deterministic Markov Processes) can be used for compositional specification of systems from the class of stochastic hybrid processes formed by PDPs (Piecewise Deterministic Markov Processes). We define CPDPs and the composition of CPDPs, and prove that the class of CPDPs is closed under composition.
Strubbe, S.N., van der Schaft, Arjan
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On Risk-Sensitive Piecewise Deterministic Markov Decision Processes [PDF]
Applied Mathematics & Optimization, 2018 We consider a piecewise deterministic Markov decision process, where the expected exponential utility of total (nonnegative) cost is to be minimized. The cost rate, transition rate and post-jump distributions are under control. The state space is Borel, and the transition and cost rates are locally integrable along the drift.
Guo, Xin, Zhang, Yi
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Change-point detection for piecewise deterministic Markov processes [PDF]
Automatica, 2018 We consider a change-point detection problem for a simple class of Piecewise Deterministic Markov Processes (PDMPs). A continuous-time PDMP is observed in discrete time and through noise, and the aim is to propose a numerical method to accurately detect both the date of the change of dynamics and the new regime after the change.
Cleynen, Alice, de Saporta, Benoîte
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Non-equilibrium Thermodynamics of Piecewise Deterministic Markov Processes
Journal of Statistical Physics, 2009 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
FAGGIONATO A +2 more
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Piecewise deterministic Markov processes and their invariant measures [PDF]
Annales de l'Institut Henri Poincaré, Probabilités et Statistiques, 2021 Piecewise Deterministic Markov Processes (PDMPs) are studied in a general framework. First, different constructions are proven to be equivalent. Second, we introduce a coupling between two PDMPs following the same differential flow which implies quantitative bounds on the total variation between the marginal distributions of the two processes.
Durmus, Alain +2 more
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Infinite dimensional Piecewise Deterministic Markov Processes
Stochastic Processes and their Applications, 2023 In this paper we aim to construct infinite dimensional versions of well established Piecewise Deterministic Monte Carlo methods, such as the Bouncy Particle Sampler, the Zig-Zag Sampler and the Boomerang Sampler. In order to do so we provide an abstract infinite-dimensional framework for Piecewise Deterministic Markov Processes (PDMPs) with unbounded ...
Paul Dobson, Joris Bierkens
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