Peter Guttorp

Stochastic Modeling of Scientific Data (eBook, PDF)

• Format: PDF

Produktbeschreibung

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.

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 384
• Erscheinungstermin: 29. März 2018
• Englisch
• ISBN-13: 9781351413664
• Artikelnr.: 54156925

Autorenporträt

Guttorp\, Peter

Inhaltsangabe

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

---

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.

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