Sentence examples for mean log likelihood from inspiring English sources

The true number of subpopulations (K) was determined by plotting a graph of K versus Delta K using mean log likelihood for each K (Ln P (D)) as described in the tutorial of the STRUCTURE software (Additional file 1: Figure S1).

Mean log likelihood (lnL), branch lengths and topologies were compared across four replicate analyses to insure that a stable posterior distribution was reached.

The mean log likelihood was estimated for the data for a given k (referred to as L K)) in each run.

Bayes Factors were computed by subtracting the harmonic mean of the -log likelihood of the posterior distribution of trees post burnin from the unconstrained analysis from that of the constrained analysis [(B10  = (Harmonic Mean –log Likelihood H1) – (Harmonic Mean –log Likelihood H0)] [76].

We therefore calculated the consensus tree by sampling trees only from the run associated with the higher mean log likelihood.

We estimated the mean log likelihood of the data for a given k (referred to as L K)) in each run.

This process was repeated 20 times, and the total average for mean log-likelihood at fixed K was used.

The consensus tree topology, the arithmetic mean log-likelihood (lnL) for this topology, and branch support were estimated from the set of sampled trees with the best posterior probabilities.

Log-Bayes factors were computed by taking the differences in the harmonic mean log-likelihoods averaged over all four runs between the 'two tree' model and the 'one tree' model.

For the SSR dataset the mean log-likelihood curve attained a maximum value around K = 6, beyond which the mean log-likelihood values reached a plateau and the standard deviations associated with the estimates increased.

The mean log-likelihood and the Δ K statistics were calculated using Structure Harvester software version 0.6.93 (Earl 2012).