Stochastic Modeling of Scientific Data

Peter Guttorp (University of Washington, Seattle, USA)

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English

Chapman & Hall/CRC
18 December 2020

Probability & statistics; Stochastics; Biology, life sciences

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.

The 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 analyzed 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 analyzing hidden Markov models is presented.

The strength of this text lies in the use of informal language that makes the topic more accessible to non-mathematicians.

The combinations of hard science topics with stochastic processes and their statistical inference puts it in a new category of probability textbooks.

The numerous examples and exercises are drawn from astronomy, geology, genetics, hydrology, neurophysiology and physics.

By: Peter Guttorp (University of Washington Seattle USA)
Imprint: Chapman & Hall/CRC
Country of Publication: United Kingdom
Dimensions: Height: 234mm, Width: 156mm,
Weight: 453g
ISBN: 9780367449001
ISBN 10: 0367449005
Pages: 384
Publication Date: 18 December 2020
Audience: Professional and scholarly , Undergraduate
Format: Paperback
Publisher's Status: Active

Guttorp, Peter

Reviews for Stochastic Modeling of Scientific Data

"""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"