FIRST LOOK AT STOCHASTIC PROCESSES, A

Rosenthal Jeffrey S

ISBN 10: 9811208972 / ISBN 13: 9789811208973

Edité par World Scientific Sep 2019, 2019

A propos de cet article

Neuware - This textbook introduces the theory of stochastic processes, that is, randomness which proceeds in time. Using concrete examples like repeated gambling and jumping frogs, it presents fundamental mathematical results through simple, clear, logical theorems and examples. It covers in detail such essential material as Markov chain recurrence criteria, the Markov chain convergence theorem, and optional stopping theorems for martingales. The final chapter provides a brief introduction to Brownian motion, Markov processes in continuous time and space, Poisson processes, and renewal theory.Interspersed throughout are applications to such topics as gambler's ruin probabilities, random walks on graphs, sequence waiting times, branching processes, stock option pricing, and Markov Chain Monte Carlo (MCMC) algorithms. The focus is always on making the theory as well-motivated and accessible as possible, to allow students and readers to learn this fascinating subject as easily and painlessly as possible.

N° de réf. du vendeur 9789811208973

Détails bibliographiques

• Titre: FIRST LOOK AT STOCHASTIC PROCESSES, A
• Éditeur: World Scientific Sep 2019
• Date de parution: 2019
• Langue: anglais
• ISBN à 10 chiffres: 9811208972
• ISBN à 13 chiffres: 9789811208973
• Reliure: Taschenbuch
• État de l'article: Neu
• Dimensions: 229 L x 152 H x 12 P (en millimètres) 229 millimètres de largeur par 152 millimètres de hauteur par 12 millimètres de profondeur
• Poids: 318 grammes

A propos de ce titre

Synopsis

This textbook introduces the theory of stochastic processes, that is, randomness which proceeds in time. Using concrete examples like repeated gambling and jumping frogs, it presents fundamental mathematical results through simple, clear, logical theorems and examples. It covers in detail such essential material as Markov chain recurrence criteria, the Markov chain convergence theorem, and optional stopping theorems for martingales. The final chapter provides a brief introduction to Brownian motion, Markov processes in continuous time and space, Poisson processes, and renewal theory. Interspersed throughout are applications to such topics as gambler's ruin probabilities, random walks on graphs, sequence waiting times, branching processes, stock option pricing, and Markov Chain Monte Carlo (MCMC) algorithms. The focus is always on making the theory as well-motivated and accessible as possible, to allow students and readers to learn this fascinating subject as easily and painlessly as possible.