Sentence examples for likelihood follows from inspiring English sources
Of course, when H implies E or the negation of E, or when H is a statistical hypothesis that bestows a certain chance on E, then the likelihood follows "analytically".
(Even evaluating the number of components would be difficult, because of the irregular parameter space in the mixture model [ 34, 35], the logarithm of the likelihood follows a complicated mixture of chi-square distributions [ 36]).
Since each region of the genome where these chromatin types are located is closely independent of each other, the likelihood follows as: ∏ ξ j ∈ A ∏ i ∈ ξ j Pr (C i, ξ j | Data, Θ ), where C i, ξ j are the counts associated with arm ξ j for chromatin type i and Θ are the unknown model parameters that must be estimated.
Our technique first examines the candidate generating the highest likelihood, followed by the candidate generating the second highest likelihood, then by the candidate generating the third highest likelihood and so forth until all candidates (four in our case) are examined.
Hence, fusion order (1,2,3,4) means that it first examines the candidate generating the highest likelihood, followed by the candidate generating the second highest likelihood, then by the candidate generating the third highest likelihood, and finally followed by the fourth and last candidate.
Our fusion order first examines the candidate generating the highest likelihood, followed by the candidate generating the second highest likelihood, then by the candidate generating the third highest likelihood, and so forth until all candidates (four in our case) are examined.
Transcription intensities in adjusted log2 were estimated after normalization within arrays using maximum likelihood followed by between array variance stabilization.
Because we did not have historical source population samples from Russian Karelia, the isolation model (m = 0) was chosen, and maximum likelihood followed that described in Wang [ 39].
The difference between these log-likelihoods follows a chi-square-distribution, and the greater the difference, the more "informative" that index is.
It was estimated by iterative generalized least square, first-order marginal quasi-likelihood followed by second-order penalized quasi-likelihood.
Instead of finding maximum likelihood, EM involves the complete data log likelihood as follows: begin{aligned} l_{c}left( theta right) =sum _{n=1}^{N}log,pleft( x_{n},z_{n}mid theta right).
