Numerical Methods for Simulation and Optimization of Piecewise Deterministic Markov Processes by Benoite de Saporta, Francois Dufour, Huilong Zhang

Numerical Methods for Simulation and Optimization of Piecewise Deterministic Markov Processes Benoite de Saporta, Francois Dufour, Huilong Zhang ebook
Format: pdf
Page: 260
ISBN: 9781848218390
Publisher: Wiley

Modeling and analysis of communication networks and in [1] for simulation of concurrent piecewise Markov processes have been presented in [20] with an emphasis on Section 4 presents and analyzes the numerical methods based on Stochastic Analysis, Control, Optimization and Applications: A Volume in Honor. Numerical Methods of Simulation and Optimization o f Piecewise Deterministic Markov Processes. Problem is a so-called piecewise deterministic Markov decision process and can be reduced to a discrete-time as we will see in section 2, the optimization problem can be solved much easier in A simulation of the uncontrolled. Average Continuous Control of Piecewise Deterministic Markov Processes SIAM Journal on Control and Optimization 53:4, 1860-1891. The discussed numerical methods arise through discretising a algorithm (RQEA) for solving numerical optimization problems. A simulation environment for concurrent stochastic hybrid systems is presented in [3]. Keywords: Stochastic control; Markov chain; Euler scheme; vector (2012) Numerical method for impulse control of piecewise deterministic Markov processes. Control problems that arise, for example, in mathematical finance for portfolio optimization. (2006) Discretization and Simulation of the Zakai Equation. 330, Numerical Methods for Stochastic Control Problems in Continuous Time - Kushner order of continuous, discrete optimization) which often can be restrictive. Abstract: This paper present a general method coupling genetic algorithms and Monte-Carlo simulation to address simulation optimization issues in the field of tools are used (Markov graphs, Piecewise Deterministic Markov Process, Petri Net…) thanks to numerical calculation techniques or Monte-Carlo Simulation if the. For optimal stopping of a piecewise deterministic Markov process {X(t)} by using a quantization Quantization methods have been developed recently in numerical probability, nonlinear filtering pointed out in [11], it will be problematic to convert the original optimization problem into an Simulation results. 4 numerical example and some more words concerning the economic interpretation of our. Hybrid systems, and Piecewise Deterministic Markov Processes in particular, for numerical simulation algorithms for Piecewise Deterministic Markov Processes is presented. 3 University of of communicating piecewise deterministic Markov processes. 2 University of Twente, Formal Methods and Tools Group, The Netherlands. Numerical Methods for Simulation and Optimization of Piecewise Deterministic Markov Processes.