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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.
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).
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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).
An expectation maximization algorithm is used to infer the parameters of the mixture of distributions.
A Gibbs sampling method was developed to infer the parameters of this model.
Here we introduce a new statistical framework for inferring the recombination parameters, including the rate of bias in recombination, from a sample of bacterial genomes.
Details about the methodology for inferring the tidal parameters are given in Andrioli et al. (2009).
Other forthcoming applications could be validating the method using real data recordings, both for inferring parameters of the model and synaptic conductances.
We develop an optimization framework for inferring parameters of the logistic functions for a given patient population via cyclic block coordinate descent.
For one: the parameters of the trials.
Resampling techniques provide methods to infer sampling distribution properties without assuming a distribution function or knowing analytical expressions for the parameters of the distribution.
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