Markov Chain
Course: 6.01
Developers: Laura Breiman and So yeun (Ashley) Cho
Mentor: Rob Miller
GitHub: https://github.com/laurabreiman/markov-chain
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Demo
This applet, using a particular lego game example, helps students visualize how a Markov chain transitions the probability distribution of each state in discrete time. It also helps students to understand the application of Bayes' Theorem in updating the probability distribution. For use in the MIT class 6.01(Introducation to Electrical Engineering and Computer Science) to understand Markov chain and Bayes' Theorem.
Screenshots:
Wiki: A Markov chain is a stochastic model describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event. A countably infinite sequence, in which the chain moves state at discrete time steps, gives a discrete-time Markov chain (DTMC).
