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In the analysis of the combined loci, parameter estimates of each locus were unlinked, allowing independent substitution models for each locus.
For priors of mutation parameters, only the Uniform and the Gamma distributions are considered, but hierarchical schemes are possible, with a mean mutation rate or coefficient P (of the geometric distribution in the GSM) drawn from a given prior and individual loci parameter values drawn from a gamma distribution around the mean.
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Marker loci parameters (map position, number of alleles, observed informative meioses etc).
With the development of analytical methods for multiple-locus parameter estimation and the relative ease of collecting large amounts of sequence data, researchers with limited resources are concerned increasingly with how much data is required to estimate the parameters of interest accurately [17].
The percentiles adjusted for the baseline expected under the neutral scenario give a less biased estimate of multi-locus parameter deviations than the raw data, while the large number of resampling allows an estimation of the chance of observing each rare combination of sequential values without additional models or assumptions.
Bayesian analyses for the combined data set were performed in a partitioned framework, allowing locus-specific parameter estimation.
More recent breeding, quantitative genetic, and modeling studies generally found positive contributions of Glu-3 loci to parameters related to dough strength, extensibility and bread-making quality [13], [30], [32] [36], [38], [56], [57].
With 100 or 1,000 loci, all parameters are well estimated.
Indeed, even with L = 10 loci, all parameters except θ0 are well estimated.
For each gene, the mutation rate μ was estimated from the per-locus mutation parameter ?
For unknown loci, these parameters can only be guessed, but the expectation is that the relative risks will usually be small and therefore the required samples large.
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