Markov Process
Markov Process is a random process whose future states solely depend on the present state. That is, the future states of the process is independent from its past history.
Applications
Markov Process can be applied in a number of ways to many different fields.
- Weather modeling: A sunny day will likely to follow another sunny day. Using this logic, the probabilities of weather conditions can be given by a transition matrix. For example, WGEN Weather Generation Model uses a transition matrix to produce a whether pattern.
- Economics and Finance: A Markov chain can used to model switches between periods of high volatility and low volatility of asset returns.
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