Markov Models

Markov Models

Last was mm06

+ MM01: Describe general idea and types of problems that lend themselves to Markov model

A markov model consists of a set of "states" and a complete description of the likelihood of transitions between the states. If a system consists of entities that can be in mutually exclusive states (e.g., immune, susceptible, sick, dead) between which transitions are probabilistic (with each tick of the clock a known fraction of sick people will die, a known fraction will recover and become immune) then a Markov model may be useful to understand the long term behavior of the system. (EDIT)

+ MM02: Describe requirements of a system to be eligible and how to solve "something else" problems

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+ MM03: Translate between description, transition matrix, diagram

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+ MM04: From diagram, set up equations in Excel, chart results, identify equilibrium states

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+ MM05: Terms and concepts

Markov chain vs. Markov process, regular, absorbing, cyclical, state, absorbing state, transient state, eigenvector, transition matrix (EDIT)

+ MM06: Suggest indications and contraindications, identify and respond to typical concerns and caveats

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