Sentence examples for mixture distribution and from inspiring English sources

In Equation (9), M and y is the mixture distribution and the number of distributional components in the mixture distribution, respectively.

The BMIX++ (UC Berkeley mechanistic MIXing code in C++) code has been developed to accurately and efficiently predict the fluid mixture distribution and heat transfer in large stratified enclosures for accident analyses and design optimizations.

For the SA population the Lognormal/Weibull and Normal/Weibull assumption both fit the model well and the prevalence was 30.3% (95% CI: 29.4 31.1).The distribution of observed (histogram), mixture distribution and component distribution of tuberculous infection and cross-reactions by country is shown in Figure 1.

The expressions of targets and non-targets were modeled by a two-component Gaussian mixture distribution, and the nucleotide frequencies at each position of an extended binding motif were assumed to be multinomial which was represented by a position specific weight matrix (PSWM).

In each simulation, univariate probe signal intensities (similar to MLPA) have been generated from a gaussian mixture distribution, and copy number status has been inferred from them.

To model counts from multiple source populations, Wang et al. (1996) proposed multi-component Poisson mixture distributions, and their approach has been extended to other finite mixtures of non-degenerate count distributions.

The problem of finding the Kalai Nash solution to a simple bargaining game belongs to a broad class of equivalent problems in Statistics and Operations Research which includes finding nonparametric maximum likelihood estimators for finite mixture distributions and constructing Bayesian D-optimal designs for one-parameter nonlinear models (South African Statist. J. 32 (1998) 43).

Similarly, properties of the variances of the observed subpopulations can be estimated from the properties of mixture distributions, and they contain an upwards bias proportional to Δ2.

The robustness of the mixture Bayesian network learning process granted by the use of mixture distributions and potentially variable numbers of mixture components across the network was investigated.

It requires certain parametric assumptions about the curves and underlying mixture distributions, and the results may be highly sensitive to the initial partitioning of observations into clusters.

This mixture distribution method and the method based on curve-fitting of translation function derived from mapping of cumulative distribution functions are illustrated to be capable of capturing the upper tail of translation function, thus lead to satisfactory estimations of extreme statistics for a variety of non-Gaussian processes.