The combined score is computed by integrating the likelihoods from the different types of evidence, correcting for the probability of randomly observing an interaction [ 101].
Combined hazard integrates the likelihood of occurrence with the probability of a particular volume range.
We propose a Bayesian approach with a Markov random field (MRF) model to integrate the likelihood function derived from observed experimental data, with prior information (i.e., empirical molecular mechanics energies) about the protein structures.
Some sites evolve much more slowly than others, and the ideal way to account for this is to integrate the likelihood at each site over the (unknown) relative evolutionary rate of that site, using a prior distribution over the relative rates such as a gamma distribution.
It is obvious that integrates the likelihood,, of the future observation over the uncertainty about the model parameters encoded in the posterior distribution p(θ| y).
When numerous covariables are put in the model, the NLMIXED procedure may fail to integrate the likelihood or give non-stationary estimations.
To compute the evidences we need to integrate the likelihood, P(D| ω, σ, c, H n, I) from equation (8), over ω, σ, and c for each model H n. By assigning proper normalised priors to all model parameters it is possible to integrate over them around the maximum likelihood estimate.
As shown in eq. 1, the total log-likelihood score (LL total ) for a pair of alleles integrates the likelihoods of the allele pair given the observed data over individual SNP sites (LL geno ) and across multiple sites (LL phase ), together with the baseline probability of the allele pair in human (LL freq ).
The tracked target is divided into several fragments by integrating the log-likelihood ratio image and morphological operation, and each fragment is tracked through a kernel using the mean shift procedure.
The observed data likelihood is obtained by integrating the completed data likelihood over all the unobserved x's, and then maximized for MLE.
