Sentence examples for likelihood distribution from inspiring English sources

We study the problem of projecting a distribution onto (or finding a maximum likelihood distribution among) Markov networks of bounded tree-width.

To gain a quantitative understanding of these differences, the experimental data were analyzed using our mathematical model, and a Markov chain Monte Carlo (MCMC) method was used to determine the likelihood distribution of the parameters characterizing the replication efficiency of each of the four viruses.

The variance of the spatial likelihood distribution that is constructed with the intersection is thus lower.

The parameter ranges are split into m bins (in our study m = 40) of equal width, and the gradient of the cumulative likelihood distribution in each bin is calculated from the difference of the cumulative likelihood distribution between adjacent bins.

In this study, we estimate the spatial likelihood distribution of a tremor source as described in Scherbaum et al. (1997).

Horizontal and vertical cross-sections of the likelihood distribution for the source location of the continuous tremor.

Horizontal and vertical cross-sections of the likelihood distribution for the source location of the explosion earthquakes.

Here, we have M different likelihood distributions.

Basing on likelihood distributions, it is possible to calculate cumulative distribution functions for targets and impostors.

Evaluation is therefore based on analysis of target and impostor likelihood distributions.

However, Hamacher- and multi-PHAT likelihood distributions have greater peakiness with more likelihood mass concentrated around the talker.