prob140.MarkovChain.empirical_distribution

MarkovChain.empirical_distribution(starting_condition, n, repetitions)

Finds the empirical distribution

Parameters:

starting_condition : state or distribution

Starting state or distribution of starting state

n : int

number of steps

repetitions : int

number of repetitions

Returns:

Table

Distribution after n steps over a certain number of repetitions

Examples

>>> mc = Table().states(make_array("A", "B")).transition_probability(make_array(0.5, 0.5, 0.3, 0.7)).toMarkovChain()
>>> start = Table().states(make_array("A", "B")).probability(make_array(.8, .2))
>>> mc.empirical_distribution(start, 10, 100)
Value | Proportion
A     | 0.4
B     | 0.6