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Applied Stochastic Modeling [electronic resource] / by Liliana Blanco-Castañeda, Viswanathan Arunachalam.

By: Blanco-Castañeda, Liliana [author.].
Contributor(s): Arunachalam, Viswanathan [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Synthesis Lectures on Mathematics & Statistics: Publisher: Cham : Springer Nature Switzerland : Imprint: Springer, 2023Edition: 1st ed. 2023.Description: VII, 151 p. 17 illus., 13 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783031312823.Subject(s): Statistics  | Mathematics | Mathematical statistics -- Data processing | Mathematical analysis | Statistics | Applied Statistics | Mathematics | Applications of Mathematics | Statistics and Computing | AnalysisAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 519.5 Online resources: Click here to access online
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.
In: Springer Nature eBookSummary: 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. .
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Discrete-Time Markov Chain -- Branching Processes and Hidden Markov Model -- Poisson Processes and its Extensions -- Continuous-Time Markov Modeling -- Applications and Biology and Ecology.

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. .

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