Displaying similar documents to “Dynamic system evolution and Markov chain approximation.”

A mathematical framework for learning and adaption: (generalized) random systems with complete connections.

Ulrich Herkenrath, Radu Theodorescu (1981)

Trabajos de Estadística e Investigación Operativa

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The aim of this paper is to show that the theory of (generalized) random systems with complete connection may serve as a mathematical framework for learning and adaption. Chapter 1 is of an introductory nature and gives a general description of the problems with which one is faced. In Chapter 2 the mathematical model and some results about it are explained. Chapter 3 deals with special learning and adaption models.

Fast simulation for road traffic network

Roberta Jungblut-Hessel, Brigitte Plateau, William J. Stewart, Bernard Ycart (2001)

RAIRO - Operations Research - Recherche Opérationnelle

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In this paper we present a method to perform fast simulation of large markovian systems. This method is based on the use of three concepts: Markov chain uniformization, event-driven dynamics, and modularity. An application of urban traffic simulation is presented to illustrate the performance of our approach.