Simulation and modeling - Old Questions

If the future states of a process are independent of the past and depend only on the present , the process is called a Markov process. A discrete state Markov process is called a Markov chain. A Markov Chain is a random process with the property that the next state depends only on the current state.

Markov chains are used to analyze trends and predict the future. (Weather, stock market, genetics, product success, etc.

Key features of Markov chains

1. The outcome of each experiment is one of a set of discrete state.

2. The outcome of the experiment depends only on the present state and not on the past state.

3. The transition probability remains constant from one to the next.