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Stochastic Modeling of Scientific Data combines stochastic modeling and statistical inference in a variety of standard and less common models, such as point processes, Markov random fields and hidden Markov models in a clear, thoughtful and succinct manner.
List of contents
Table of contents; Discrete time Markov chains; Continuous time Markov chains; Markov random fields; Point processes; Brownian motion and diffusion; Appendix 1: Statistical theory for the multinomial distribution; Appendix 2: Linear difference equations with constant coefficients; Appendix 3: Some theory of partial differential equations.
About the author
Guttorp, Peter
Summary
Stochastic Modeling of Scientific Data combines stochastic modeling and statistical inference in a variety of standard and less common models, such as point processes, Markov random fields and hidden Markov models in a clear, thoughtful and succinct manner.
Additional text
"The author's lucid presentation of his material together with this very great number of applications from life sciences, make this an excellent buy for only thirty pounds for every biometrician."
-Biometrics
"When it comes to introducing Markov chains, everyone talks about the weather but nobody does anything about getting real data. In this book, though, we get not only the pattern of rainfall in Snoqualmie Falls, Washington, but wind directions in South Africa, and interarrival times of cyclones in the bay of Bengal. The objecive is to provide an introduction to stochastic processes suited to those who while not necessarily shy of mathematics, are primarily interested in problems with the flavor of real life...still, even hard-bitten mathematical probabilists may find new insights in this insistently realistic approach."
-Zentralblatt fur Mathematik
