Markov Models: Summary

Markov Model A collection states and the transitions between them.
Three representations:
• A set of conditional probabilities describing the state transitions.
• A graph where the vertices are the states and the edge weights are the probability of moving from one state to another.
• A transition matrix where the entry at row r and column c is the probability of moving from state r to state c.
The time course of the system can be found by simulating its development using Excel.
The final equilibrium state of the system can be found by solving a set of simultaneous equations based on the transition matrix.

Playback speed: 1.0x1.5x 2.0x