prob140.MarkovChain.simulate_path

MarkovChain.simulate_path(starting_condition, steps, plot_path=False)

Simulates a path of n steps with a specific starting condition.

Parameters:

starting_condition : state or Distribution

If a state, simulates n steps starting at that state. If a Distribution, samples from that distribution to find the starting state.

steps : int

Number of steps to take.

plot_path : bool

If True, plots the simulated path.

Returns:

ndarray

Array of sampled states.

Examples

>>> states = ['A', 'B']
>>> transition_matrix = np.array([[0.1, 0.9],
...                               [0.8, 0.2]])
>>> mc = MarkovChain.from_matrix(states, transition_matrix)
>>> mc.simulate_path('A', 10)
array(['A', 'A', 'B', 'A', 'B', 'A', 'B', 'B', 'A', 'B', 'B'])