Nature's probability of flipping either coin does not actually depend on the agent's prediction, so we can replace the conditional probabilities p0(θ|x) by p0.
Moreover, we see that in our case, nature's probability of flipping either coin does not actually depend on the agent's prediction, so we can replace the conditional probabilities p(θ|x) by p. We have then an inner variational problem: arg max p ~ ∑ θ p ~ - 1 β log p ~ p 0 + U ( x, θ ) (14).
The forgetting parameter φ also affects the model's prediction, so we ran simulations in which φ was set at 0 (i.e., no forgetting over the whole season), as in [2], [7].
SNPs that are not associated to a trait may add errors to a prediction so we repeated the previous analyses by restricting the SNPs used to construct genomic relationships to those that had consistent phases (defined above) in both breeds.
This error prediction gives a kind of range of variation for the predictions, so that we may infer with 95% confidence the predictions are correct and in our case the predicted responses of the model indicate a good fit overall.
However, experience with real docking applications shows that the scoring stage needs to be very flexible to account for the nature of each complex and the data available (see, for example, [ 31- 36], but BiGGER has been used in over 70 published complex predictions) and so we should consider filtering and ranking the models as two distinct problems.
The numerator in equation (5) is a sum over prediction errors; so, we can look at the error associated with a given local authority by fixing i and summing over j to get the outward commuting error.
14813 mutations across 4052 protein sequences received both a B-SIFT score and a SNAP prediction, and so we focused on these mutations for further analysis.
The transit peptide-like domains of bipartite plastid targeting sequences often attain poor prediction scores, so we used the NCBI (http://www.ncbi.nlm.nih.gov/) Conserved Domain Search (http://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi) [132] to identify N-terminal extensions from the conserved regions of the respective protein.
These 9 features constitute the smallest subset of features that make the classifiers have the best prediction performance, so we select them as our selected subset of features [ 19, 20].
