Sentence examples for likelihood evidence from inspiring English sources

It deals with the uncertainty generated by the multivariate contribution plots and improves the diagnostic capacity by updating the BN with multiple likelihood evidence.

Furthermore, with the emergence of a plausible mechanism, the drift hypothesis hypothesis is no longer so overwhelmingly implausible prior to taking the likelihood evidence into account.

Although this kind of "evidence" may not be representable via evidential likelihoods (because the hypotheses it bears on don't deductively or probabilistically imply it), it often plays an important role in scientific assessments of hypotheses — in assessments of whether a hypothesis is so extraordinary that only really extraordinary likelihood evidence could rescue it.

(1) posterior = prior ∗ likelihood evidence (2) P (θ | D, H i ) = P (θ | H i ) P (D | θ, H i ) P (D | H i ) In high dimensions, log Z can be computed effectively by a nested sampling strategy that exploits statistical properties of the shrinkage of the prior volume.

The theorem shows how, through the likelihoods, evidence combines with prior probabilities (prior plausibility assessments) to produce posterior probabilities (posterior plausibility values) for hypotheses.

In commuting the death sentence, Mr. Strickland, a Democrat, said that he believed it was still likely that Mr. Keith committed the murders, but that he was troubled by the likelihood that evidence uncovered since his conviction would not be presented to a court before the scheduled Sept. 15 execution.

Since the training set is fixed, the likelihood and evidence densities are in fact time invariant.

Deduction uses the complementary equation P(Evidence|Hypothesis) to compute the likelihood of evidence given a hypothesis.

We propose models for both likelihood and evidence densities, required for the sequential estimation process, as described in Eqs.

So, we have: Notice that if a catch-all hypothesis is needed, the likelihood of evidence relative to it will not generally enjoy the same kind of objectivity as the likelihoods for specific, positive hypotheses.

For the first analysis run in BayesTraits (comparing Model A to Model B), we found that, while Model B had a slightly better marginal likelihood, the evidence to support this was not strong (i.e., Bayes Factor <2) (Table 1).