With this option, it is possible to estimate the transition probabilities of n-grams involving out-of-vocabulary words.
42.
The likelihood of a phylogeny is computed from a nested sum of transition probabilities that corresponds to the hierarchical structure of the proposed tree.
43.
Let X be a finite set and let K ( x, y ) be the transition probability for a reversible Markov chain on X.
44.
This requirement is quite restrictive as a linear change will not, in general, be the optimal profile to achieve the desired transition probability.
45.
In addition, there is no need for transition probabilities as a result of the fact that context information is encoded within the context profiles.
46.
This is valid for every bond configuration the system can pass through during its evolution, so detailed balance is satisfied for the total transition probability.
47.
Because any one transition probability can be determined once the others are known, there are a total of N ( N-1 ) transition parameters.
48.
The transition probability matrix can be derived for endogenous vs . acquired genes, from which Bayesian posterior probabilities for particular stretches of DNA can be obtained.
49.
Reinforcement learning can solve Markov decision processes without explicit specification of the transition probabilities; the values of the transition probabilities are needed in value and policy iteration.
50.
Reinforcement learning can solve Markov decision processes without explicit specification of the transition probabilities; the values of the transition probabilities are needed in value and policy iteration.
