Sentence examples for conditional of the model from inspiring English sources

During each MCMC simulation we also sampled from the full conditional of the model and data to construct predictive intervals (PI) that defined a 95% probability of future observations being within the PI range.

†Posterior probabilities that the ancestral residues are correct, conditional on the model of sequence evolution used.

The fit of different models of diversification was assessed by comparing their respective marginal likelihoods, which are defined as the probability of the data D conditional on the model M, p(D| M).

In the following, I derive the full conditionals of the model.

The chained data augmentation algorithm applied to interval censored data imputes candidates of possible event times conditional to the model and the data observed.

This paper addresses the problem of computing posterior probabilities in a discrete Bayesian network where the conditional distributions of the model belong to convex sets.

And then combining the high efficient state dependent parameter (SDP) method for the calculation of the conditional moments of the model output, a SDP solution is established to solve two moment-independent importance measures.

A common diagnostic technique for stochastic point processes, called Residual Analysis (Ogata, 1988), is to transform the time axis t to a new scale by the increasing function (4 where Tstart is the starting time of observation history, is the region under study and is the conditional intensity of the model, parameterized by maximum likelihood parameters.

This makes the conditional entropy of the model and the neuron quite similar as well.

Because of the conditional structure of the model, the log likelihood components from each time point are independent and so can be summed to allow a single overall fit using standard logistic regression.

Bayesian inference stems from the posterior distribution, that is, the conditional distribution of the model parameters given the observed data p θ | C h o l R, where θ denotes the vector of all model parameters, Chol R ) the data vector, p represents the probability density function.