prob140.MarkovChain.distribution

MarkovChain.distribution(starting_condition, n)

Finds the distribution of states after n steps given a starting condition

Parameters:

starting_condition : state or Distribution

The initial distribution or the original state

n : integer

Number of transition steps

Returns:

Table

Shows the distribution after n steps

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(0.8, 0.2))

>>> mc.distribution(start, 0)

State | Probability

A | 0.8

B | 0.2

>>> mc.distribution(start, 2)

State | Probability

State | Probability

A | 0.392

B | 0.608

>>> mc.distribution(start, 10000)

State | Probability

A | 0.375

B | 0.625