A stochastic process is a mathematical model that evolves over time in a probabilistic manner. There is special kind of stochastic process, called Markov chain, where the outcome of an experiment depends only on the outcome of the previous experiment. In other words, the next state of the system depends only on the present state, not on preceding states. Applications of Markov chains in medicine are quite common and have become a standard tool of medical decision making. Markov chains are named after the Russian mathematician A. A. Markov (1856–1922), who started the theory of stochastic processes.
Run the python script:
python main.py- Choose between the following options:
- Enter matrix manually
- 3)Calculate probability from going to one state to another in n steps
- 4)Calculate long-term(steady) state of the matrix
- 5)Identify if matrix is regular or not
- 6)Exit
- Generate random stochastic matrix
- 3)Calculate probability from going to one state to another in n steps
- 4)Calculate long-term(steady) state of the matrix
- 5)Identify if matrix is regular or not
- 6)Exit
- Exit
Image 1. Enter matrix manually and steady state
Image 2. Random Stochastic Matrix and steady state
[1]Bolch, G., 2006. Queueing networks and Markov chains. 2nd ed. Hoboken, N.J.: Wiley.