All results in the following sections were computed using our implementation of the above methods, Bayesfit and Bayescompare; the former may be used for inferring the parameters of the model for a single growth curve and the latter for the detection of differences in growth rate (via hypothesis testing).
In the following, we first describe the probabilistic model underlying our method (more fully explained in Section 2) and an efficient algorithm for inferring the parameters of the model from sequence alignments.
To infer the parameters of the fitness function for each selection that we performed, we fit the distribution ρ and parameters μ* and τ that lead to an optimal fit to the observed distributions p(x) and p′(x).
In order to infer the parameters of these phylogenetic hypotheses for each pair of sequences, we used the Muse-Gaut model [ 110] of codon substitution with nucleotide equilibrium frequencies based on their position in the codon (in Hyphy nomenclature MG94W9).
These observations suggest that a sample size of at least 3000 diploid individuals might be needed for inferring the parameters associated with the simulated recent generalized growth epoch, which is motivated by previous models of European demographic history.
A Bayesian two-component mixture model was used to infer the parameters of the beta binomial distributions for each of the alleles and to compute the posterior probability of each sequence having originated from each allele, based on the observed methylation data.
In order to infer the parameters relevant for the modelled game, thus bridging this gap, a set of experiments is performed on the real-life software-defined radio SDR/CR test bed.
We inferred the parameters only for the most recent epoch, which is of generalized growth in one model while limited to exponential growth in the other.
We executed Dispom [ 20] (version 1.5), which discovers de novo motifs significantly over-represented at the promoter region of target genes using a learning process to infer the parameters, 20 times for each dataset and discarded the results with a P-value > 1e-4 or those whose distance between the 75 and 25 position distribution percentile is > 50 bp.
We then used our method to infer the parameter values for each of the 11 data sets.
