Sentence examples for marginal likelihood between from inspiring English sources

Twice the difference in loge space of marginal likelihood between any two models is the Bayes Factor, 2loge(BF).

The difference (in loge space) of marginal likelihood between any two models is the loge of the Bayes Factor, loge(BF).

The Bayes factor is the ratio of the marginal likelihoods between two hypotheses, whereby the marginal likelihoods are here estimated in Tracer.

Bayes factors calculate the ratio of marginal likelihoods between two given models by integrating over all possible parameter values (as opposed to estimating the maximum likelihood for each parameter).

Various different substitution, coalescent and molecular clock models were compared by calculating Bayes Factors (BF), which is the difference in log marginal likelihoods between two model combinations [ 25, 26].

We examined at each node if there is support for one area over another: we constrained the ancestral state of the node to one area and compared the harmonic means (an estimator of marginal likelihoods) between runs under different constraints by calculating the Bayes factor between them.

On the other hand, differences in the marginal likelihood value between the strict and relaxed molecular clock models were below the significance threshold of 1.3 generally accepted (log10BF = 0.49, Table 3).

However, it can effectively be attacked by alternating between optimizing the marginal likelihood as a function of p0 and the set of pattern locations, {x i,i = 1…M}, respectively.

After a run of 1 million iterations in BayesTraits [15] to estimate the correlation between leaf-living ecology and reduced morphology, the following harmonic means of the marginal likelihood were obtained, with virtually no difference between runs: ∼71.50 for the model in which leaf-living ecology and reduced morphology are independent, and ∼56.50 for the model in which they are dependent.

However there are a couple of methods of approximating the marginal likelihood of a model (and therefore the BF between two models) by processing the output of a BEAST analysis.

Gaussian processes allow marginalizing the latent function to obtain a marginal likelihood.