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Just as in the previous section, I compute the APE for the marginal likelihoods of childbearing and labor force participation as the weighted average of the partial derivatives of (4) across the sample, with the share of high-skilled women represented by each age-MSA-year cell used as weights.
We then compared model likelihoods by calculating Bayes Factors (BF, the ratio of the marginal likelihoods of two models).
We calculated the marginal likelihoods of the post-burn-in posterior distribution for each partition and used Mesquite [64] to generate the two consensus trees.
Approximate marginal likelihoods of the different demographic models were calculated with enforced strict and relaxed molecular clocks (Table 3, 4 and 5).
The use of Bayes Factors applies logic similar to that used in likelihood ratio tests, except the marginal likelihoods of two models are compared rather than their maximum likelihoods.
We calculated approximate marginal likelihoods of six different demographic models (supplemental Table S2): four parametric models (constant population size with strict or relaxed molecular clock, exponential growth with strict or relaxed molecular clock), and two non-parametric models (Bayesian skyline plot, BSP, with strict or relaxed molecular clock).
Similar(34)
This is performed by maximising the marginal likelihood of the Gaussian process.
The algorithm provides both posterior samples and an estimate of the evidence (marginal likelihood) of the model.
\] This expression is often called the marginal likelihood of the model: it expresses how probable the data is in the light of the model as a whole.
The MLE of (μ,D,σ2) is determined by the marginal likelihood of the observed data L(Y o ;μ,D,σ2), whereas this quantity is often intractable.
The harmonic mean method calculates an approximation of the marginal likelihood of a fitted model, while perplexity measures how well a fitted model can predict unseen data.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com