When correlation in model structure exists, two strategies were used to improve predictive performance by retaining structurally distinct models or assigning smaller prior model probabilities to correlated models.
In our simulations, we assumed equal prior model probabilities to represent prior ignorance.
We combined these information sources in a Bayesian framework that allowed us to model probability densities to describe potential natal origins for each of the 867 individual harvested HY scaup [c.f. 22], [23].
Similarly, the negative likelihood ratio was calculated as the ratio of the proportion of individuals with a BRCA1/2 mutation who have a model probability <10% to the proportion of individuals without a mutation who have a model probability <10%.
For, averaging over the model probabilities according to the quantum-equilbrium distribution, the model will reproduce the statistics of orthodox quantum mechanics.
Other implications for tort law are discussed, as are the sensitivity of the results to the choice of how to model probability errors.
This approach removes subjectivity in estimating temporal weights between intervals of unequal length and uses observed movements and measurable location error to model probability of occurrence.
Another advantage is that extensive theoretical and practical applications of MCMC methods show that they are extremely robust and flexible approaches to model probability distributions [ 5].
A discrimination criterion is developed based on posterior model probability that directly uses data to evaluate model importance.
