Sentence examples for function of the probabilistic from inspiring English sources

By maximizing the likelihood function of the probabilistic model, TEMPI jointly estimates the TDNs showing temporal transitions of network structures with temporal activation of the GOBPs and their temporal interplays.

After applying TEMPI to these synthetic input data (X I, T, E ) for the 616 DRNs, the output variables (X t, G t, A t ) were estimated by the optimization of the likelihood function of the probabilistic graphical model (see Section 2.2).

In this study, we proposed a novel low-rank approximation based integrative probabilistic model to fast find the shared principal subspace across multiple data types: the convexity of the low-rank regularized likelihood function of the probabilistic model ensures efficient and stable model fitting.

The BF uses an array of m bits and k independent hash functions for the probabilistic representation of a set of items ({S} = {s_1,s_2, ldots, s_n}).

In this study, the convex linearization is constructed by utilizing the sensitivity and function value of the probabilistic constraint at the MPP.

Conceptually, we can think of the winner-take-all analysis as effectively approximating the likelihood in terms of periodic boxcar functions; for the probabilistic analysis, we treat the likelihood as a periodic sum-of-Gaussians.

The Monte Carlo simulation and the first-order second moment method are applied to establish the limit-state function of probabilistic analysis of the replacement thickness.

The sensed sequence of 2D image data, which may lose some 3D information and is usually noisy, includes the target, similar objects, and cluttered background, etc. Bayesian filter attempts to construct the posterior probability density function of the target state using the probabilistic formulations.

In this case, the maximum of the upper bound of confidence interval of design objective is selected as the objective function, while the minimal value of the probabilistic constraint is selected as the constraint function.

For binary trees, we furthermore discuss several constrained variants of the MPP as well as a partition function approach to the probabilistic version of the MPP.

Their work depends exclusively on probability functions, and the probabilistic interpretation of the concepts of producer and consumer risk some years before Neyman and Pearson (1933) offered their seminal interpretation of type I and type II error.