## Markov Process Demonstration

This applet demonstrates a Markov chains and Markov processes, which are commonly used stochastic models where the future behaviour of the system depends solely on the current state that the system is in. Usage:

- In
**Add State**mode you can add new states into the model. - In
**Add Transition**mode you can add new transitions by first clicking the source state and then dragging the mouse pointer over the destination state. - In
**Enumerate States**mode you can enumerate the states by clicking the state in the order you prefer. - In
**Edit**mode you can adjust the transition probabilities (note: simulator does not crosscheck the validity of the entered values, self-transition is assumed if the sum of probabilities is less than 1.0) - In
**Move**mode you can move the states in the area or reshape the transition curves. - In
**Delete**mode you can delete both states and transitions by clicking them. - The simulations are started in
**Simulate**mode by clicking the initial state with the mouse. Simulation can be stopped by another click. - The next option can be used to set the simulation speed.
- The last options contains a small set of examples.

**References:**

- Markov chain from Wikipedia
- Markov chain from Mathworld
- S-38.143 Queueing theory course lecture notes on Stochastic processes and Markov chains

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Esa Hyytiä, 2004.