Sentence examples for following likelihood from inspiring English sources

As a result, they can be found via maximizing the following likelihood function: ∏ i = 1 L p y i | α, β = ∏ i = 1 L ∫ p y i | θ i, β p θ i | α d θ i. (8).

The following likelihood function is used to evaluate the goodness-of-fit of each model: This likelihood function assumes that model errors are normally distributed and that stochastic events on each population are primarily driven by external environmental effects [32].

We assume the following likelihood model for the data: (1) where μ k is the parameter of a Binomial distribution for genotype k. μ k models the expectation that for a given genotype k, a randomly sampled allele will be the reference allele.

The GLRT uses the following likelihood ratio test (LRT) for the case of two sensors: (3).

A modified generalized likelihood ratio test (mGLRT) proposed here uses the following likelihood ratio for decision: (4).

Each feature is generated by a mixture of Gaussian distributions, with the following likelihood given the estimated GMM mixture weights, means, and standard deviations : (20).

We additionally tested whether the transmission rate from sperm typing was compatible with that inferred from the TDT, using the following likelihood-ratio test: LR = Lik(θsperm = θTDT /Lik(θsperm, θTDT), where θ represents the paternal transmission rate of the TDT-based overrepresented allele at SNP rs9381373; for this test, we combined allele counts from unselected and selected sperm.

Specifically, within the 68-TTLVA-72 68-TTLVA-72 68-TTLVA-72have the following likelihoodsequenceng a capping residue, normalized theamino acid occuresidues1.13 for T68, 1.41 for T69, 0.84 for L70, 0.70 for V71, and 1.43 for A72.

Models with different fixed effect structures were compared using likelihood ratio tests following maximum likelihood model fitting.

Following [10], the k th code c k  is declared as detected if the following generalized likelihood ratio test (GLRT) is satisfied: Λ k ( θ ̂ k ) ∑ m = 0 M - 1 ∥ X ( m ) ∥ 2 ≥ λ (3).

The most straightforward way is to maximize the likelihood following the log-likelihood gradient.