Stochastic Modeling of Scientific Data, Second Edition
Series: Chapman & Hall/CRC Texts in Statistical Science;
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Product details:
- Edition number 2, New edition
- Publisher Chapman and Hall
- Date of Publication 30 May 2025
- ISBN 9781439875643
- Binding Hardback
- No. of pages384 pages
- Size 234x156 mm
- Language English 700
Categories
Short description:
Accessible to any scientist or student with a reasonable background in statistics, this updated book presents a unique blend of stochastic process theory, statistical inference, and scientific applications of stochastic modeling of scientific data. With an emphasis on real data applications, this second edition covers the numerous developments in methodology and computation since the original publication. It provides a more balanced treatment of Bayesian approaches to statistical inference and includes more recent scientific example problems that involve the statistical analysis of stochastic models.
MoreLong description:
Praise for the First Edition:
The author's lucid presentation of his material, together with this very great number of applications from life sciences, make this an excellent buy ? .
?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
Table of Contents:
Stochastic Processes Background. Statistical Preliminaries. Inference for Discrete-Time Markov Chains. Inference for Continuous-Time Markov Chains. Markov Random Fields. Point Processes. Continuous-Time, Continuous-Space Processes. Case Studies.
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