Sentence examples for a fast likelihood from inspiring English sources

To estimate the model parameters, we use SASeis2006 (Ogata 2006), which estimates the five ETAS parameters (μ, K, c, α, p) by minimizing the value of the Akaike Information Criterion (Akaike 1974) with a fast likelihood algorithm (Ogata et al. 1993).

Its major strength is a fast maximum likelihood tree search algorithm that returns trees with good likelihood scores.

Our automated preprocessing step polishes the data and rejects too small and too large distance circles and uses a fast maximum likelihood algorithm on the distance.

In order to infer the relationship of the 222 nucleotide sequences retrieved from NCBI and ENSEMBL, a fast maximum likelihood method of tree reconstruction, as implemented in GARLI [17], was used with the default options.

Phylogenetic trees were created using RAxML 7.0.4 (Stamatakis et al. 2005), a fast maximum likelihood (ML) phylogenetic program, and MrBayes (Ronquist and Huelsenbeck 2003).

New phylogenies were built using a fast maximum likelihood algorithm as implemented in RAxML v7.0 [ 50], after aligning length-variable regions with MAFFT v5.8 [ 51].

Phylogenetic analyses were performed in a two-step manner: First, a fast maximum likelihood phylogenetic inference of each orthologous group was performed to identify and remove inparalogs (sequences that appeared duplicated only inside a taxon) and misclassified orthologs genes that were grouped in an orthologous group erroneously, due to partial similarity of sequence.

Some aspects of this problem are commonly encountered in both spotting and large vocabulary continuous speech recognition (LVCSR) contexts, such as fast likelihood computation [8, 9] or fast acoustic matching [10, 11].

A phylogeny of the full Mcm2-9 protein family was reconstructed from the amino acid alignments of each conserved MCM domain [ 5, 32] by performing a fast Maximum-likelihood (ML) analysis using PHYML [ 33] and a Bayesian analysis using MRBAYES [ 34].

CarthaGene looks for multiple populations' maximum likelihood consensus maps using a fast EM algorithm for maximum likelihood estimation and powerful ordering algorithms.

For each phosphorylated serine, we have reconstructed the evolution of this site in the corresponding taxonomical group using a fast modification of the maximum likelihood algorithm (A. Goland, in preparation).