In this variant implementation, one can specify some uncertainty for a given leaf, by letting leaf_state return true for several states. In that case, it is understood that each state has equal probability. If leaf_state always returns false for a given leaf, it is interpreted as the leaf being unobserved.
val pruning :
('a, 'b, 'c) Tree.t ->
nstates:int ->
transition_probabilities:('c -> matrix_decomposition) ->
leaf_state:('b -> int -> bool) ->
root_frequencies:Linear_algebra.vec ->
floatpruning t ~nstates ~transition_probabilities ~leaf_state
~root_frequencies returns the probability of observing the states returned by leaf_state at the leaves of t given the CTMC specified by nstates, transition_probabilities and root_frequencies.
val conditional_likelihoods :
('n, 'l, 'b) Tree.t ->
nstates:int ->
leaf_state:('l -> int -> bool) ->
transition_probabilities:('b -> Linear_algebra.mat) ->
(shifted_vector, int array, 'b * Linear_algebra.mat) Tree.tval conditional_simulation :
Gsl.Rng.t ->
(shifted_vector, int array, 'b * Linear_algebra.mat) Tree.t ->
root_frequencies:Linear_algebra.vec ->
(int, int, 'b * Linear_algebra.mat) Tree.t