Markov Chain Process (Theory and Cases)


Author(s): Carlos Polanco *

Pp: 1-7 (7)

DOI: 10.2174/9789815080476123010006

* (Excluding Mailing and Handling)


In this section we review the main Probability operators that are strongly associated with the main themes of this book which are Discrete-Time Markov Chain Process, and Continuous-Time Markov Chain Process. the chapter begins with the basic definition of: certain event, dependent event, independent event and impossible event. Later we review the concept of conditional probability which permeates all the following chapters as well as the multiplication rule. At the end the Bayes’ Theorem is addressed which is the basis of the procedures described in the last chapters as they are: Discrete and Continuous-Time Markov Chain Process. All sections are exemplified in the simplest and most complete way possible, so that the reader does not have difficulty in the use and language of these operators in the following sections.

Keywords: Bayes’ Theorem, Certain Event, Conditional Probability, Dependent Random Events, Impossible Event, Independent Random Events, Multiplication Rule, Random Event

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