Estimation of Probability Distribution and Its Application in Bayesian Classification and Maximum Likelihood Regression

Nonparametric estimation of cumulative distribution function and probability density function of continuous random variables is a basic and central problem in probability theory and statistics. Although many methods such as kernel density estimation have been presented, it is still quite a challengi...

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Veröffentlicht in:	Interdisciplinary sciences : computational life sciences Jg. 11; H. 3; S. 559 - 574
Hauptverfasser:	Dai, Hao, Wang, Wei, Xu, Qin, Xiong, Yi, Wei, Dong-Qing
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2019 Springer Nature B.V
Schlagworte:	Algorithms Bayes Theorem Bayesian analysis Biomedical and Life Sciences Classification Computational Biology - methods Computational Biology/Bioinformatics Computational Science and Engineering Computer Appl. in Life Sciences Computer simulation Continuity (mathematics) Distribution functions Economic models Health Sciences Life Sciences Likelihood Functions Mathematical and Computational Physics Mathematical models Maximum likelihood estimation Medicine Models, Statistical Monte Carlo simulation Numerical methods Original Research Article Probability Probability density functions Probability distribution Probability theory Random variables Regression Analysis Regression models Research Design Spline functions Statistical analysis Statistical methods Statistics for Life Sciences Statistics, Nonparametric Theoretical Theoretical and Computational Chemistry Distribution function estimation Density function estimation Bayesian classification Spline regression Smoothing spline Maximum likelihood regression
ISSN:	1913-2751, 1867-1462, 1867-1462
Online-Zugang:	Volltext
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