Research ArticleOpen accessIn the keywordsBayesian Exploration of Multilocus Interactions on the Genome-Wide ScaleIvan Kozyryev, Jing ZhangProblem statement: Recent technological and scientific advances propelled the field of Genome-Wide Association Study (GWAS), which promises to be instrumental in linking many common complex diseases to their genetic origin. While so far such large-scale surveys have been moderately successful in identifying disease related genetic variants, much of disease heritability is still not accounted for by the discovered loci. There … …Current Research in Bioinformatics, Vol. 1, Issue 1 · 2 Jul 2012 · 7,401 accessesView PDFCite
Research ArticleOpen accessIn the abstractBayesian Statistical Inference for Number Counting ExperimentsDiego CasadeiStatistical inference describes how to infer about the true but unknown population from the measured sample and is a fundamental ingredient in scientific data analysis. Often one knows the probability model and wishes to estimate its parameters. The Bayesian approach provides a solution in terms of the posterior probability density function of the parameters of interest, given the model, the … …American Journal of Engineering and Applied Sciences, Vol. 8, Issue 4 · 27 Nov 2015 · 7,570 accessesView PDFCite
Research ArticleOpen accessIn the abstractCOMBINING LONG MEMORY AND NONLINEAR MODEL OUTPUTS FOR INFLATION FORECASTHeri Kuswanto, Irhamah, Laylia AfidahLong memory and nonlinearity have been proven as two models that are easily to be mistaken. In other words, nonlinearity is a strong candidate of spurious long memory by introducing a certain degree of fractional integration that lies in the region of long memory. Indeed, nonlinear process belongs to short memory with zero integration order. The idea of the forecast … …Journal of Mathematics and Statistics, Vol. 10, Issue 3 · 7 Jul 2014 · 6,981 accessesView PDFCite
Research ArticleOpen accessIn the abstractANALYSIS 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 accessIn the abstractSMOOTHING 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 accessIn the abstractBayesian 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