Stochastic Population Processes: Analysis, Approximations, Simulations by Eric RenshawStochastic Population Processes: Analysis, Approximations, Simulations by Eric Renshaw

Stochastic Population Processes: Analysis, Approximations, Simulations

byEric Renshaw

Paperback | April 19, 2015

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The vast majority of random processes in the real world have no memory - the next step in their development depends purely on their current state. Stochastic realizations are therefore defined purely in terms of successive event-time pairs, and such systems are easy to simulate irrespective oftheir degree of complexity. However, whilst the associated probability equations are straightforward to write down, their solution usually requires the use of approximation and perturbation.
Eric Renshaw graduated from Imperial College, London with a B.Sc. in Mathematics, studied for the Diploma in Statistics at Manchester University, and then researched for his M.Phil. and Ph.D. degrees in spatial stochastic population processes at Sussex and Edinburgh Universities. He joined the staff of Edinburgh University in 1969, fi...
Title:Stochastic Population Processes: Analysis, Approximations, SimulationsFormat:PaperbackDimensions:672 pages, 9.69 × 6.73 × 0 inPublished:April 19, 2015Publisher:Oxford University PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0198739060

ISBN - 13:9780198739067


Table of Contents

Preface1. Introduction2. Simple Markov Population Processes3. General Markov Population Processes4. The Random Walk5. Markov Chains6. Markov Processes in Continuous Time and Space7. Modelling Bivariate Processes8. Two-Species Interaction Processes9. Spatial Processes10. Spatial-Temporal ExtensionsReferencesIndex