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The implemented likelihood-ratio test [ 67] employs the EM algorithm [ 68] to infer haplotypes from unphased genotypic data to test for statistical significance of LD.
For the NP detector to be implemented, both likelihood functions must be known.
A test for positive selection was implemented using likelihood ratio tests that compare models pair M1a/M2a (Yang 2007).
We implemented the likelihood ratio test as an online tool (http://www.humangenomeprivacy.org/service.php) for participating teams to evaluate the privacy risks in their perturbed data.
We implemented this likelihood function in a Markov Chain Monte Carlo framework and applied the Metropolis-Hastings algorithm [ 62, 63] to sample the posterior distribution of the birth-death model parameters.
In the site model, codon site models M0, M3, M7, and M8 were implemented, using likelihood ratio tests to test whether variable ω (dN/dS) ratios were present at the amino acid sites.
To overcome this inconvenience, we reconstructed the phylogenetic tree for the 371 mammalian species being analyzed and implemented maximum-likelihood models for our prepartitioned data sets.
The EM algorithm is an algorithm implementing maximum likelihood estimation.
This software implements several likelihood models, which can be used to test different hypotheses about selection at loci of interest.
We compared BBM results to results from Lagrange, which implements a likelihood method and the Dispersal-Extinction-Cladogenesis (DEC) model [ 81].
MPP was also used to implement a likelihood approach with Neighbor-Joining to compensate for the single reference design (Additional File 1: table S1E).
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