Stochastic process : Probability theory, Deterministic system (mathematics), Gillespie algorithm, Markov chain, Stochastic calculus, Dynamics of Markovian particles

Miriam T. Timpledon

ISBN 10: 6130302231 / ISBN 13: 9786130302238

Published by Omniscriptum, 2010

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online.In probability theory, a stochastic process, or sometimes random process, is the counterpart to a deterministic process. Instead of dealing with only one possible 'reality' of how the process might evolve under time (as is the case, for example, for solutions of an ordinary differential equation), in a stochastic or random process there is some indeterminacy in its future evolution described by probability distributions. This means that even if the initial condition (or starting point) is known, there are many possibilities the process might go to, but some paths are more probable and others less.

Seller Inventory # 9786130302238

Bibliographic Details

• Title: Stochastic process : Probability theory, Deterministic system (mathematics), Gillespie algorithm, Markov chain, Stochastic calculus, Dynamics of Markovian particles
• Publisher: Omniscriptum
• Publication Date: 2010
• Language: English
• ISBN 10: 6130302231
• ISBN 13: 9786130302238
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 5D millimeters 220 millimeters width by 150 millimeters height by 5 millimeters depth
• Weight: 125 grams

About this title

Synopsis

Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online.In probability theory, a stochastic process, or sometimes random process, is the counterpart to a deterministic process. Instead of dealing with only one possible reality" of how the process might evolve under time (as is the case, for example, for solutions of an ordinary differential equation), in a stochastic or random process there is some indeterminacy in its future evolution described by probability distributions. This means that even if the initial condition (or starting point) is known, there are many possibilities the process might go to, but some paths are more probable and others less."