Applied Stochastic Modeling
This book provides the essential theoretical tools for shastic 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 shastic nature.


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Applied Stochastic Modeling
This book provides the essential theoretical tools for shastic 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 shastic nature.


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Applied Stochastic Modeling

Applied Stochastic Modeling

Applied Stochastic Modeling

Applied Stochastic Modeling

Overview

This book provides the essential theoretical tools for shastic 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 shastic nature.


Product Details

ISBN-13: 9783031312847
Publisher: Springer Nature Switzerland
Publication date: 06/19/2024
Series: Synthesis Lectures on Mathematics & Statistics
Edition description: 2023
Pages: 151
Product dimensions: 6.61(w) x 9.45(h) x (d)

About the Author

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 shastic 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 shastic processes and its applications in biology, reliability and queuing theory, and statistics of financial markets.

Table of Contents

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