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10: Markov Chains

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    37826
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    Learning Objectives

    In this chapter, you will learn to:

    1. Write transition matrices for Markov Chain problems.
    2. Explore some ways in which Markov Chains models are used in business, finance, public health and other fields of application
    3. Find the long term trend for a Regular Markov Chain.
    4. Solve and interpret Absorbing Markov Chains.

    Thumbnail: A diagram representing a two-state Markov process, with the states labelled E and A. Each number represents the probability of the Markov process changing from one state to another state, with the direction indicated by the arrow. For example, if the Markov process is in state A, then the probability it changes to state E is 0.4, while the probability it remains in state A is 0.6. (CC BY-SA 3.0; Joxemai4 via Wikipedia)


    This page titled 10: Markov Chains is shared under a CC BY 4.0 license and was authored, remixed, and/or curated by Rupinder Sekhon and Roberta Bloom via source content that was edited to the style and standards of the LibreTexts platform.