Python Specials #11 Discrete-Time Markov Chain Simulation
We simulate the Markov Chain for T = 10 time steps, and store the state history in a list called state_history. We use numpy.random.choice
to randomly sample the next state based on the transition probabilities given by the transition matrix P.
The code uses matplotlib.pyplot.plot to plot the state history as a line plot. The x-axis shows the time step, and the y-axis shows
the state of the Markov Chain. We also add labels for the axes and a title for the plot. Finally, we use matplotlib.pyplot.show to display
the plot. Note that since the Markov Chain is stochastic, the state history may be different each time we run the code.
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