Description
Book SynopsisThis practical introduction covers mathematical methods for the analysis of stochastic models and their biological applications. Based on courses taught at the University of Oxford, the book can be used for self-study or as a supporting text for advanced undergraduate or beginning graduate-level courses in applied mathematics.
Trade Review'The text can be used effectively for solitary study or as a textbook for a course offered at the boundary between undergraduate and beginning graduate study … This is a remarkable, even admirable, work that bears the mark of its Oxford origins. Its potential audience includes chemists and mathematicians as well as adventuresome biologists and physicists and perhaps even bright or intrepid general readers.' A. E. Viste, Choice
'This textbook is an example-driven introduction to stochastic modeling in mathematical biology … Beyond serving as a course textbook, the book could serve as a good general introduction to the area of stochastic modeling in biology for researchers, particularly given the copious citations to more specialist texts.' Andrew Krause, MAA Reviews
'Erban and Chapman's Stochastic Modelling of Reaction–Diffusion Processes will be valuable both as a reference for practitioners and as a textbook for a graduate course on stochastic modelling. Every chapter includes problems for the reader. The problems are well written and appropriate for most intended readers of the book. I hope that this book is widely adopted and that it becomes a standard textbook in the field.' Michael A. Salins, Mathematical Reviews/MathSciNet Review
'This book is also available at a reduced price as an e-book on Kindle. Based on the sample I viewed, all the features of the printed book have been perfectly preserved, with no loss of clarity in the layout or the mathematical symbols or the graphs and diagrams.' David Hopkins, The Mathematical Gazette
Table of Contents1. Stochastic simulation of chemical reactions; 2. Deterministic versus stochastic modelling; 3. Stochastic differential equations; 4. Diffusion; 5. Efficient stochastic modelling of chemical reactions; 6. Stochastic reaction-diffusion models; 7. SSAs for reaction-diffusion-advection processes; 8. Microscopic models of Brownian motion; 9. Multiscale and multi-resolution methods; Appendix A. Deterministic modelling of chemical reactions; Appendix B. Discrete probability distributions; Appendix C. Continuous probability distributions; References; Index.
