Model probabilities are determined based on simple statistical arguments.
The generation probability (Pgen) of a nucleotide or amino acid CDR3 is the sum of all model probabilities of V(D J recombination events that generate the sequence.
Based on experience from probabilistic seasonal climate and medium-range forecasting, calibration of model probabilities is necessary to account for model deficiencies and produce reliable forecasts.
The variances of groundwater head predictions are significantly decreased by reducing posterior model probabilities of unimportant propositions.
The standard procedure in such a case is to use the expected likelihood in computing the posterior model probabilities.
The significant computational cost of evaluating posterior model probabilities renders traditional Bayesian methods infeasible when the model space becomes large.
Model probabilities for ENSO-neutral conditions are between 90% and 95% from Oct-Dec 2013 to Jan-Mar 2014, dropping steadily during northern spring 2014, to become near or just below 50% by northern summer.
The present paper presents and discusses a new sequential discrimination procedure, which makes use of model probabilities and concentrates the efforts on models with higher probabilities.
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.
First, the BMA tree of posterior model probabilities permits the comparative evaluation of the candidate propositions of each uncertain model component.
It is also shown that model probabilities may be interpreted in kinetic terms and properly correlated to the reaction polymerization temperature.
