BAYES

作品数：847被引量：2478H指数：18
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Practical Use of the Subjective Mathematical Model of Bayes and Its External Validation in Dental Medicine & Dentistry
《Open Journal of Statistics》2024年第5期553-575,共23页Muyembi Muinaminayi Pierre Kayembe Mwimbi David Nyimi Boshabu Fidèle Panoumvita Kapamona Junior Nsudila Mpoyi Monique Tubanza Mulongo Simplot Sekele Issouradi-Bourley Jean-Paul Mantshumba Milolo Augustin Kalala Kazadi Em Mabela Rosti Munyanga Mukongo Sylvain Dan Wang 
Objective: Our study aims to validate the subjective Bayes mathematical model using the mathematical model of logistic regression. Expert systems are being utilized increasingly in medical fields for the purposes of a...
关键词：External Validation-MSB-MRL Mathematical Model 
Use of BayesSim and Smoothing to Enhance Simulation Studies
《Open Journal of Statistics》2017年第1期153-172,共20页Jeffrey D. Hart 
The conventional form of statistical simulation proceeds by selecting a few models and generating hundreds or thousands of data sets from each model. This article investigates a different approach, called BayesSim, th...
关键词：Loss Function BAYES Risk Prior DISTRIBUTION Regression SIMULATION SKEW-NORMAL DISTRIBUTION GOODNESS of Fit 
Statistical Classification Using the Maximum Function被引量：1
《Open Journal of Statistics》2015年第7期665-679,共15页T. Pham-Gia Nguyen D. Nhat Nguyen V. Phong 
The maximum of k numerical functions defined on , , by , ??is used here in Statistical classification. Previously, it has been used in Statistical Discrimination [1] and in Clustering [2]. We present first some theore...
关键词：MAXIMUM DISCRIMINANT Function Pattern Classification NORMAL Distribution BAYES Error L1-Norm Linear QUADRATIC Space CURVES 
Linear Dimension Reduction for Multiple Heteroscedastic Multivariate Normal Populations
《Open Journal of Statistics》2015年第4期311-333,共23页Songthip T. Ounpraseuth Phil D. Young Johanna S. van Zyl Tyler W. Nelson Dean M. Young 
For the case where all multivariate normal parameters are known, we derive a new linear dimension reduction (LDR) method to determine a low-dimensional subspace that preserves or nearly preserves the original feature-...
关键词：Linear TRANSFORMATION BAYES Classification FEATURE Extraction PROBABILITY of MISCLASSIFICATION 
Correct Classification Rates in Multi-Category Discriminant Analysis of Spatial Gaussian Data被引量：1
《Open Journal of Statistics》2015年第1期21-26,共6页Lina Dreiziene Kestutis Ducinskas Laura Paulioniene 
This paper discusses the problem of classifying a multivariate Gaussian random field observation into one of the several categories specified by different parametric mean models. Investigation is conducted on the clas...
关键词：Gaussian Random Field Bayes Classification Rule Pairwise Discriminant Function Actual Correct Classification Rate 
Subjectivity in Application of the Principle of Maximum Entropy被引量：1
《Open Journal of Statistics》2013年第6期1-8,共8页Jan Peter Hessling 
Complete prior statistical information is currently required in the majority of statistical evaluations of complex models. The principle of maximum entropy is often utilized in this context to fill in the missing piec...
关键词：Maximum ENTROPY BAYES Monte Carlo Uncertainty COVARIANCE DETERMINISTIC Sampling Testable Information Model Calculation Simulation 
Inferences under a Class of Finite Mixture Distributions Based on Generalized Order Statistics
《Open Journal of Statistics》2013年第4期231-244,共14页Abd EL-Baset A. Ahmad Areej M. AL-Zaydi 
The main purpose of this paper is to obtain estimates of parameters, reliability and hazard rate functions of a heterogeneous population represented by finite mixture of two general components. The doubly Type II cens...
关键词：Generalized Order STATISTICS BAYES Estimation Heterogeneous POPULATION MONTE Carlo Integration MONTE Carlo Simulation 
Minimum Description Length Methods in Bayesian Model Selection: Some Applications
《Open Journal of Statistics》2013年第2期103-117,共15页Mohan Delampady 
Computations involved in Bayesian approach to practical model selection problems are usually very difficult. Computational simplifications are sometimes possible, but are not generally applicable. There is a large lit...
关键词：BAYESIAN Analysis Model Selection Minimum DESCRIPTION LENGTH HIERARCHICAL BAYES BAYESIAN COMPUTATIONS 
Bayes Prediction of Future Observables from Exponentiated Populations with Fixed and Random Sample Size
《Open Journal of Statistics》2011年第1期24-32,共9页Essam K. AL-Hussaini M. Hussein 
Bayesian predictive probability density function is obtained when the underlying pop-ulation distribution is exponentiated and subjective prior is used. The corresponding predictive survival function is then obtained ...
关键词：Predictive Density And SURVIVAL Functions One- And Two-Sample Schemes BAYES PREDICTION Exponentiated Population. Exponentiated BURR Type XII Distribution Data Of Carbon Fibers 
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