prob140.MarkovChain.log_prob_of_path

MarkovChain.log_prob_of_path(starting_condition, path)

Finds the log-probability of a path given a starting condition

Note that for long paths, log_prob_of_path will give much better precision than np.log(prob_of_path)

Parameters:

starting_condition : state or Distribution

If a state, finds the probability of the path starting at that state. If a Distribution, finds the probability of the path with the first element sampled from the Distribution

path : array

Array of states

Returns:

float

log of probability

Examples

>>> mc = Table().states(make_array("A", "B")).transition_probability(make_array(0.5, 0.5, 0.3, 0.7)).toMarkovChain()
>>> mc.log_prob_of_path('A', make_array('A', 'B','B'))
-1.742969305058623
>>> start = Table().states(make_array("A", "B")).probability(make_array(.8, .2))
>>> mc.log_prob_of_path(start, make_array('A', 'A', 'B','B'))
-1.9661128563728327