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Both methods involve evaluating the global likelihood function.
The local PF performed at each node thereby has access to the global likelihood function, and is guaranteed to converge to the optimal filter asymptotically.
The first one uses average consensus to calculate the global likelihood at each node and a quantization step to eliminate the discrepancy between particle sets at different nodes.
With this approximation, a consensus procedure is carried out to calculate the sum of the exponential term of global likelihood function which can be expressed as the product of all the local likelihood functions.
It is likely that global likelihood maximum was indeed reached at K=2 to K=15.
However, TS2 analysis did not improve the global likelihood of the inference over TS1.
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We use alignments derived from an all-on-all sequence comparison to define domains within protein sequences based on a global maximum likelihood model.
Given are the global maximum likelihood at the root node, the AIC, and the Akaike weight (w) of the respective dispersal models.
Figure 6 shows these samples (blue dots) scattered about the verified global maximum likelihood estimate (MLE) of the triplet obtained in [ 5, 26] and subsequently confirmed algebraically in [ 23].
Multiple starting points were used for each run and the progress of the fitting algorithm was monitored to determine, so far as possible, that convergence was to the global maximum likelihood.
Analyses of models fitting shifts in selection for each lineage, and corresponding null models, ran with five different starting values of K a /K s (0.001, 0.01, 0.1, 1, and 10) to ensure the algorithm reached the global maximum likelihood optimum, rather than a local optimum.
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