The Solution According Finite Mixture Distribution by GMM Problem as One of the Modes of Expression of a Probability Density Function | Zendy

Kiyoshi Tsukakoshi | Zendy; Kenichi Ida | Zendy; Takao Yokota | Zendy

Open Access

The Solution According Finite Mixture Distribution by GMM Problem as One of the Modes of Expression of a Probability Density Function

Author(s) -

Kiyoshi Tsukakoshi,

Kenichi Ida,

Takao Yokota

Publication year - 2013

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2013.09.297

Subject(s) - probability density function , density estimation , estimator , computer science , multivariate kernel density estimation , parametric statistics , independent and identically distributed random variables , nonparametric statistics , probability distribution , range (aeronautics) , wavelet , random variable , function (biology) , algorithm , mathematics , statistics , artificial intelligence , variable kernel density estimation , kernel method , materials science , evolutionary biology , support vector machine , composite material , biology

Probability density estimation is an important tool in Data Analysis and many other areas, where it is often used for exploratory data analysis or as a part of another estimator. However, the population who can express to distribution of a beautiful form which appears in the statistical textbook can hardly be found out. Then it is a problem why the probability density function is expressed. The estimation of a probability density function based on a sample of independent identically distributed observations is essential in a wide range of applications. The estimation method of Probability Density Function -- (1) a parametric method (2) a nonparametric method and (3)a semi-parametric method etc. -- it is. I n this paper, GMM problem is taken up as a semi- parametric method and We use a wavelet method as a powerful new technique. Compactly supported wavelets are particularly interesting because of their natural ability to represent data with intrinsically local properties

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