Peter Guttorp

Stochastic Modeling of Scientific Data

• Broschiertes Buch

Produktbeschreibung

This text 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. The main distinguishing feature of this work is that, in addition to probability theory, it contains statistical aspects of model fitting and a variety of data sets that are either analysed in the text or used as exercises. Markov chain Monte Carlo methods are introduced for evaluating likelihoods in complicated models and the forward - backward algorithm for analysing hidden Markov models is presented. The numerous examples and exercises drawn from astronomy, geology, genetics, hydrology, neurophysiology and physics, make this an ideal textbook for researchers, neurophysiology and physics, make this an ideal textbook for researchers, lecturers and graduate students studying statistics and probability, especially applied probability and stochastic processes.

Produktdetails

• Verlag: Taylor & Francis Ltd (Sales)
• Seitenzahl: 384
• Erscheinungstermin: 18. Dezember 2020
• Englisch
• Abmessung: 234mm x 156mm x 20mm
• Gewicht: 540g
• ISBN-13: 9780367449001
• ISBN-10: 0367449005
• Artikelnr.: 69890037

Autorenporträt

Guttorp, Peter

Inhaltsangabe

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.

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1. Introduction 2. Discrete time Markov chains 3. Continuous time Markov chains 4. Markov random fields 5. Point processes 6. Brownian Motion and Diffusion

Rezensionen

"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