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162 results for “Bayes”
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Research ArticleOpen accessEvery word in the titleFuzzy Method for Online Learning of Bayesian Network ParametersMariana D.C. Lima, Silvia M. NassarIn learning problems, there are situations where training data is not fully available at the learning time. They are incrementally generated by time, defining a type of domain called online that has among its characteristics the possibility of data failure or even missing data. In Bayesian networks, learning is divided into two categories: structure (related to the graph of conditional … …Journal of Computer Science, Vol. 15, Issue 3 · 16 Mar 2019 · 7,498 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleBayesian Approach to the Estimation of a Poisson Mean with Application to NBA Three Point AttemptsPatricia Pepple WilliamsonBayesian Credible Intervals are proposed for a Poisson mean. These intervals are compared to five classical confidence intervals found in the literature. A simulation study is performed to compare the procedures using two different criteria and it is attempted to determine which of the procedures performs best for various values of the parameter and sample size. Estimation of the number … …Journal of Mathematics and Statistics, Vol. 14, Issue 1 · 12 Nov 2018 · 7,089 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleMCMC-Fuzzy: A Fuzzy Metric Applied to Bayesian Network Structure LearningBeatriz Wilges, Ademar Crotti Junior, Silvia Modesto NassarBayesian network structure learning is considered a complex task as the number of possible structures grows exponentially with the number of variables. Two main methods are used for Bayesian network structure learning: Conditional independence, a method in which a structure is created consistently with independence tests performed on data; and the heuristic search method that explores the structure space. Hybrid … …Journal of Computer Science, Vol. 14, Issue 8 · 24 Aug 2018 · 8,243 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleBayesian Network Inference in Binary Logistic Regression: A Case Study of Salmonella sp Bacterial Contamination on Vannamei ShrimpPratnya Paramitha Oktaviana, Kartika FithriasariRecently binary logistic regression has been used to identify four factors or predictor variables that supposedly influence the response variable, which is testing result of Salmonella sp bacterial contamination on vannamei shrimp. Binary logistic regression analysis results that there are two predictor variables which is significantly affect the testing result of Salmonella sp bacterial contamination on vannamei shrimp, those are … …Journal of Mathematics and Statistics, Vol. 13, Issue 4 · 22 Dec 2017 · 7,667 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleStock Trading Using PE Ratio Based on Bayesian InferenceHaizhen Wang, Ratthachat Chatpatanasiri, Pairote SattayathamThe Price Earnings (PE) ratio is one of the most widely applied tool for the firm valuation in a security market. Unfortunately, recent academic developments in financial econometrics and machine learning have rarely looked at this tool. In the paper, we propose to formalize a process of fundamental PE ratio estimation by employing Dynamic Bayesian Network (DBN) methodology. Forward-backward inference … …Journal of Mathematics and Statistics, Vol. 13, Issue 3 · 29 Sep 2017 · 7,425 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleOn Bayesian Premium Estimators for Gamma Lindley Model under Squared Error Loss Function and Linex Loss FunctionAhmed Sadoun, Halim Zeghdoudi, Fatma Zohra Attoui, Mohamed Riad RemitaWe consider the Gamma Lindley distribution (GaL) as the conditional distribution of (X|θ,γ), we focus on the estimation of the Bayesian premium under squared error loss function (symmetric) and linear-exponential (Linex) loss function (asymmetric), using informative priors (the Gamma prior). Because of its difficulty and non-linearity, we use a numerical approximation for computing the Bayesian premium. Finally, a simulation and … …Journal of Mathematics and Statistics, Vol. 13, Issue 3 · 23 Sep 2017 · 7,043 accessesView PDFCite
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 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 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