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90 articles for “Bayesian”
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Research ArticleOpen accessEvery word in the titleAn Algorithm for Determining the Number of Mixture Components on the Bayesian Mixture Model Averaging for Microarray DataAni Budi Astuti, Nur Iriawan, Irhamah, Heri KuswantoThe major challenges on the statistical analysis of microarray data are the limited availability of samples, large number of measured variables and the complexity of distribution of the data obtained (e.g., multimodal). These phenomena could be considered in Bayesian method, used Bayesian Mixture Model (BMM) methods and Bayesian Model Averaging (BMA) methods. Modeling of Bayesian Mixture Model Averaging (BMMA) for … …Journal of Mathematics and Statistics, Vol. 11, Issue 2 · 28 Sep 2015 · 7,136 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleDiagnosis of Cardiovascular Diseases with Bayesian ClassifiersAlaa Elsayad, Mahmoud FakhrCardiovascular disease or atherosclerosis is any disease affecting the cardiovascular system. They include coronary heart disease, raised blood pressure, cerebrovascular disease, peripheral artery disease, rheumatic heart disease, congenital heart disease and heart failure. They are treated by cardiologists, thoracic surgeons, vascular surgeons, neurologists and interventional radiologists. The diagnosis is an important yet complicated task that needs to be done accurately … …Journal of Computer Science, Vol. 11, Issue 2 · 25 Mar 2015 · 7,498 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleTHREE BAYESIAN TEST INDEXES FOR NORMAL DISTRIBUTIONSYohei KawasakiIn clinical trials, data that are consistent with a normal distribution are often regarded as candidates for primary evaluation variables. In such cases, t-tests are frequently used to compare different groups of data. However, the repeated use of t-tests leads to problems with multiplicity. This study proposes three new indexes that are based on (Kawasaki and Miyaoka, 2012; 2013). These … …Journal of Mathematics and Statistics, Vol. 10, Issue 2 · 24 Apr 2014 · 6,721 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleHEURISTIC DISCRETIZATION METHOD FOR BAYESIAN NETWORKSMariana D.C. Lima, Silvia M. Nassar, Pedro Ivo R.B.G. Rodrigues et al.Bayesian Network (BN) is a classification technique widely used in Artificial Intelligence. Its structure is a Direct Acyclic Graph (DAG) used to model the association of categorical variables. However, in cases where the variables are numerical, a previous discretization is necessary. Discretization methods are usually based on a statistical approach using the data distribution, such as division by quartiles. In … …Journal of Computer Science, Vol. 10, Issue 5 · 18 Jan 2014 · 7,054 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleANALYSIS OF BAYESIAN CLASSIFIER ACCURACYFelipe Schneider Costa, Maria Marlene De Souza Pires, Silvia Modesto NassarThe naïve Bayes classifier is considered one of the most effective classification algorithms today, competing with more modern and sophisticated classifiers. Despite being based on unrealistic (naïve) assumption that all variables are independent, given the output class, the classifier provides proper results. However, depending on the scenario utilized (network structure, number of samples or training cases, number of variables), the … …Journal of Computer Science, Vol. 9, Issue 11 · 26 Sep 2013 · 8,375 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleTHE DEVELOPMENT OF AN EXPERT CAR FAILURE DIAGNOSIS SYSTEM WITH BAYESIAN APPROACHWidodo BudihartoIn this study we propose a model of an Expert System to diagnose a car failure and malfunction using Bayesian Approach. An expert car failure diagnosis system is a computer system that uses specific knowledge which is owned by an expert to resolve car problems. Our specific system consists of knowledge base and solution to diagnose failure of car from … …Journal of Computer Science, Vol. 9, Issue 10 · 5 Sep 2013 · 7,289 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleSMOOTHING SPLINE IN SEMIPARAMETRIC ADDITIVE REGRESSION MODEL WITH BAYESIAN APPROACHRita Diana, I. Nyoman Budiantara, Purhadi, Satwiko DarmestoSemiparametric additive regression model is a combination of parametric and nonparametric regression models. The parametric components are not linear but following a polynomial pattern, while the nonparametric components are unknown pattern and assumed to be contained in the Sobolev space. The nonparametric components can be approximated by smoothing spline functions. In the development of smoothing spline, the classical statistical approach … …Journal of Mathematics and Statistics, Vol. 9, Issue 3 · 12 Jun 2013 · 7,252 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleBayesian Models for Time Series with Covariates, Trend, Seasonality, Autoregression and OutliersPitsanu Tongkhow, Nantachai KantananthaBayesian methods furnish an attractive approach to time series data analysis. This article proposes the forecasting models that can detect trend, seasonality, auto regression and outliers in time series data related to some covariates. Cumulative Weibull distribution functions for trend, dummy variables for seasonality, binary selections for outliers and latent autoregression for autocorrelated time series data are used for the … …Journal of Computer Science, Vol. 9, Issue 3 · 16 Apr 2013 · 6,653 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleAn Efficient Bayesian Nearest Neighbor Search Using Marginal Object Weight Ranking Scheme in Spatial DatabasesK. Balasaravanan, K. DuraiswamyProblem statement: A database that is optimized to store and query data that is related to objects in space, including points, lines and polygons is called spatial database. Identifying nearest neighbor object search is a vital part of spatial database. Many nearest neighbor search techniques such as Authenticated Multi-step NN (AMNN), Superseding Nearest Neighbor (SNN) search, Bayesian Nearest Neighbor (BNN) … …Journal of Computer Science, Vol. 8, Issue 8 · 30 Jul 2012 · 7,149 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleBayesian and Maximum Likelihood Solutions: An Asymptotic Comparison Related to Cost FunctionMechakra Hadda, Chadli Asssia, Tiah NaceurProblem statement: Wald showed that the minimax solution is the Bayesian solution with respect to the law a priori the worst. We try to establish a similar result by comparing the Bayesian solution and the solution of maximum likelihood when the parameter space is a compact metrizable group. Approach: we take as a priori law Haar measure because we reduce … …Journal of Mathematics and Statistics, Vol. 8, Issue 2 · 4 Jul 2012 · 6,915 accessesView PDFCite