Presenter(s)

    Jordyn Hurley

    Comments

    1:15-2:30, Kennedy Union Ballroom

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    Description

    Markov Chains model stochastic processes and are used to predict random events and their outcomes. In Markov Chains, I explored the different techniques found to demonstrate practical uses within real world scenarios. A good reminder for Markov Chains is in each event, the probability depends on the state of the previous event that occurred. Markov Chains can be applied to different theories to help analyze complex ideas. The theories include:- Transition matrices- Multi-step transition probabilities and distribution vectors- Regular Markov Chains- Absorbing Markov Chains

    Publication Date

    4-23-2025

    Project Designation

    Capstone Project

    Primary Advisor

    Rebecca J. Krakowski

    Primary Advisor's Department

    Mathematics

    Keywords

    Stander Symposium, College of Arts and Sciences

    Institutional Learning Goals

    Vocation; Scholarship

    Markov Chains

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