Name: Understanding Markov Chains. Examples and Applications
SKU: 64806162
Price: 54.22 USD
Availability: InStock
Author: Nicolas Privault

portada Understanding Markov Chains. Examples and Applications

Understanding Markov Chains. Examples and Applications

Formato

Libro Físico

Editorial

Springer Verlag, Singapore

Autor

Nicolas Privault

Colección

Springer Undergraduate Mathematics Series

Año

2025

Encuadernación

Tapa Blanda

Dimensiones

23.5x15.5 cm

ISBN13

9789819540563

Understanding Markov Chains. Examples and Applications

Nicolas Privault (Autor) · Springer Verlag, Singapore · Tapa Blanda

Libro Nuevo Importado

Envío: 21 a 28 días háb.

$ 108.45$ 54.22

-50%

Costos de importación incluídos en el precio ✅

Libro Nuevo

Quedan más de 100 unidades

$ 54.22

Llega entre el 15 Sep y el 28 Sep a Quito, Pichincha. Seleccionar ubicación

Agregar a lista de deseos

Reseña del libro "Understanding Markov Chains. Examples and Applications"

This book provides an undergraduate-level introduction to discrete and continuous-time Markov chains and their applications, with a particular focus on the first step analysis technique and its applications to the computation of average hitting times and ruin probabilities. It also discusses classical topics such as recurrence and transience, stationary and limiting distributions, as well as branching processes. It starts by examining in detail two important examples (gambling processes and random walks) before presenting the general theory in the subsequent chapters. It also provides an introduction to discrete-time martingales and their relation to ruin probabilities and mean exit times, together with a chapter on spatial Poisson processes. The concepts presented are illustrated by examples, 150 exercises and 22 problems with their solutions. This book is a revised and expanded version of the previous edition, and includes additional exercises and problems with complete solutions. As in the previous book, all exercises and problems are solved in detail, with many graphs and explanatory figures.