Since its first edition, there have been many interesting developments in the use of "likelihood-free" methods of Bayesian inference for complex stochastic models. Having been thoroughly updated to reflect this, this third edition covers everything necessary for a good appreciation of stochastic kinetic modelling of biological networks in the sy
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"...stochastic modeling has drawn the attention of many researchers in biology and physiology. A textbook, with much elaboration, is highly valuable to understanding the underlying mathematical and computational methods in biological stochastic modeling. Prof Wilkinson has designed the content of this book to fill a gap in the educational text/reference books available for students/researchers learning about stochastic modeling in biological systems... This third edition book almost covers all of the material necessary for students studying stochastic kinetics modelling. The exercises in every chapter certainly illustrate the theory and concept of the book. Appendices A and B elaborate on all of the SBML code and other software associated with the book. The codes are also complemented by links to the author's webpage and a GitHub repository. The author must be appreciated for adding so many references for further reading. The content of the book is designed for a one-semester graduate-level course in stochastic modeling in biology. Thus, this book is targeted at master and graduate students in interdisciplinary subjects such as applied mathematics, computational biology, bioinformatics, biophysics, Biochemistry, and biomedical engineering."
- Chitaranjan Mahapatra, Appeared in ISCB News, January 2020
- Chitaranjan Mahapatra, Appeared in ISCB News, January 2020