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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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