Applied Stochastic Modeling

This book provides the essential theoretical tools for stochastic modeling. The authors address the most used models in applications such as Markov chains with discrete-time parameters, hidden Markov chains, Poisson processes, and birth and death processes. This book also presents specific examples with simulation methods that apply the topics to different areas of knowledge. These examples include practical applications, such as modeling the COVID-19 pandemic and animal movement modeling. This book is concise and rigorous, presenting the material in an easily accessible manner that allows readers to learn how to address and solve problems of a stochastic nature.

Addresses the most used models, such as Markov chains, Poisson processes, and birth and death processes Provides specific examples with programming codes and uses techniques for estimating model parameters

Autorentext

Liliana Blanco-Castañeda, Dr.rer.nat is a Full Professor in the Department of Statistics at the National University of Colombia, Bogotá. She earned her M.Sc. degree in Mathematics from Universidad Nacional de Colombia, Bogotá, in 1984 and Dr. rer. nat degree in Mathematics from the Johannes Gutenberg Universität in 1991. Her research interests include theory of probability, branching processes, and stochastic processes and its applications. Viswanathan Arunachalam, Ph.D., is an Associate Professor in the Department of Statistics at Universidad Nacional de Colombia, Bogotá. He earned his Ph.D. in Mathematics from the Indian Institute of Technology Madras in 1997. He also had positions at Universidad de los Andes, Bogotá, and visiting positions at Arizona State University. His research interests include stochastic processes and its applications in biology, reliability and queuing theory, and statistics of financial markets.

Klappentext

This book provides the essential theoretical tools for stochastic modeling. The authors address the most used models in applications such as Markov chains with discrete-time parameters, hidden Markov chains, Poisson processes, and birth and death processes. This book also presents specific examples with simulation methods that apply the topics to different areas of knowledge. These examples include practical applications, such as modeling the COVID-19 pandemic and animal movement modeling. This book is concise and rigorous, presenting the material in an easily accessible manner that allows readers to learn how to address and solve problems of a stochastic nature.

Inhalt
Discrete-Time Markov Chain.- Branching Processes and Hidden Markov Model.- Poisson Processes and its Extensions.- Continuous-Time Markov Modeling.- Applications and Biology and Ecology.