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Optimal mixture weights in multiple importance sampling
multiple importance sampling mixture
2015/8/21
In multiple importance sampling we combine samples from a nite list
of proposal distributions. When those proposal distributions are used to
create control variates, it is possible (Owen and Zhou, ...
INFERENCE FOR NORMAL MIXTURE IN MEAN AND VARIANCE.
The performance of mutual information for mixture of bivariate normal distributions based on robust kernel estimation
Mutual Information Kernel Density Minimum Volume Ellipsoid
2010/9/25
Mutual Information (MI) measures the degree of association between variables in nonlinear model as well as linear models. It can also be used to measure the dependency between variables in mixture dis...
New approach to mixture of the Adomian decomposition and homotopy perturbation method for approximate and analytical solution of integral equations and fractional differential equations
Homotopy perturbation method Adomian Decomposition method
2010/9/17
Nonlinear phenomena play a crucial role in applied mathematics and physics. Analytic solutions to the nonlinear equations are of fundamental importance. Various methods for obtaining Analytic solution...
Block mixture models and discrete data
cross automatic clustering mixture model city block metric
2010/9/14
This work deals with links which exist between cross clustering methods and probabilistic models when data is discrete. For this, we define the notion of metric criterion, then we show that probabilis...