Probability and Mathematical Statistics: Statistical Inference for Stochastic Processes

Ishwar V. Basawa

ISBN 10: 0120802503 / ISBN 13: 9780120802500

Editore: Academic Press, 1980

Riguardo questo articolo

Unterschrift / Widmung ohne Bezug; Ohne Umschlag; Wasserschaden / Verschmutzung; Leichte Risse. Statistical Inference Stochastic Processes provides information pertinent to the theory of stochastic processes. This book discusses stochastic models that are increasingly used in scientific research and describes some of their applications. Organized into three parts encompassing 12 chapters, this book begins with an overview of the basic concepts and procedures of statistical inference. This text then explains the inference problems for Galton-Watson process for discrete time and Markov-branching processes for continuous time. Other chapters consider problems of prediction, filtering, and parameter estimation for some simple discrete-time linear stochastic processes. This book discusses as well the ergodic type chains with finite and countable state-spaces and describes some results on birth and death processes that are of a non-ergodic type. The final chapter deals with inference procedures for stochastic processes through sequential procedures. This book is a valuable resource for graduate students.

Codice articolo 450dfde0-547c-4ea0-9e4a-88b4268d3ecc

Dati bibliografici

• Titolo: Probability and Mathematical Statistics: Statistical Inference for Stochastic Processes
• Editore: Academic Press
• Data di pubblicazione: 1980
• Lingua: Inglese
• ISBN 10: 0120802503
• ISBN 13: 9780120802500
• Rilegatura: Hardcover
• Condizione: Fair

Descrizione articolo

Riassunto

Statistical Inference Stochastic Processes provides information pertinent to the theory of stochastic processes. This book discusses stochastic models that are increasingly used in scientific research and describes some of their applications. Organized into three parts encompassing 12 chapters, this book begins with an overview of the basic concepts and procedures of statistical inference. This text then explains the inference problems for Galton–Watson process for discrete time and Markov-branching processes for continuous time. Other chapters consider problems of prediction, filtering, and parameter estimation for some simple discrete-time linear stochastic processes. This book discusses as well the ergodic type chains with finite and countable state-spaces and describes some results on birth and death processes that are of a non-ergodic type. The final chapter deals with inference procedures for stochastic processes through sequential procedures. This book is a valuable resource for graduate students.