Filters








55,741 Hits in 1.8 sec

Fully Bayesian logistic regression with hyper-LASSO priors for high-dimensional feature selection

Longhai Li, Weixin Yao
2018 Journal of Statistical Computation and Simulation  
In this paper we introduce an MCMC (fully Bayesian) method for learning severely multi-modal posteriors of logistic regression models based on hyper-LASSO priors (non-convex penalties).  ...  Our MCMC algorithm uses Hamiltonian Monte Carlo in a restricted Gibbs sampling framework; we call our method Bayesian logistic regression with hyper-LASSO (BLRHL) priors.  ...  Bayesian Logistic Regression with Hyper-Lasso Priors We will now describe our method, BLRHL, including some technical details.  ... 
doi:10.1080/00949655.2018.1490418 fatcat:cso4m5gqxrfsnazp7gfhtkepcq

Bayesian Modeling and MCMC Computation in Linear Logistic Regression for Presence-only Data [article]

Fabio Divino, Natalia Golini, Giovanna Jona Lasinio, Antti Penttinen
2013 arXiv   pre-print
We propose a new formalization for the logistic model with presence-only data that allows further insight into inferential issues related to the model.  ...  We concentrate on the case of the linear logistic regression and, in order to make inference on the parameters of interest, we present a Markov Chain Monte Carlo algorithm with data augmentation that does  ...  Conclusions In this work, we presented a Bayesian procedure to estimate the parameters of logistic regressions for presence-only data.  ... 
arXiv:1305.1232v1 fatcat:afboxdvdsrb7fbdbxzptg26tnq

Bayesian Logistic Regression using Vectorial Centroid for Interval Type-2 Fuzzy Sets

Ku Muhammad Naim Ku Khalif, Alexander Gegov
2015 Proceedings of the 7th International Joint Conference on Computational Intelligence  
Inspired by such real applications, in this research study, the theoretical foundations of Vectorial Centroid of interval type-2 fuzzy sets with Bayesian logistic regression is introduced.  ...  It also highlights the incorporation of fuzzy sets with Bayesian logistic regression allows the use of fuzzy attributes by considering the need of human intuition in data analysis.  ...  Bayesian Logistic Regression The principal of Bayesian inference for logistic regression analyses follows the typical pattern for Bayesian analysis (Joseph, 2015) : 1.  ... 
doi:10.5220/0005614400690079 dblp:conf/ijcci/KhalifG15 fatcat:l67bqdqzbbf6rn7fytdnhioefa

Dynamic Programming for Bayesian Logistic Regression Learning under Concept Drift [chapter]

Pavel Turkov, Olga Krasotkina, Vadim Mottl
2013 Lecture Notes in Computer Science  
The framework is based on the application of the Bayesian approach to the probabilistic pattern recognition model in terms of logistic regression, hidden Markov model and dynamic programming.  ...  This mechanism is based on the Bayesian approach to the logistic regression in the finite-dimensional feature space under Markov assumption on the drifting discriminant hyperplane.  ...  We use the logistic regression approach [10] , so, the probabilities of two possible class memberships of an instance y j,t = ±1 can be expressed as a logistic functions of its feature vector x j,t f  ... 
doi:10.1007/978-3-642-45062-4_26 fatcat:3mehx2mhevfzphl764wuxx3uja

Bayesian model selection for logistic regression models with random intercept

Helga Wagner, Christine Duller
2012 Computational Statistics & Data Analysis  
Risk of the event is usually modeled using a logistic regression model, with a random intercept to control for heterogeneity among clusters.  ...  Bayesian model selection is performed for a reparameterized version of the logistic random intercept model using spike and slab priors on the parameters subject to selection.  ...  Many Bayesian variable selection methods use spike and slab priors for the regression coefficients.  ... 
doi:10.1016/j.csda.2011.06.033 fatcat:nyajwsdrbzgwnaq4zvlgfuu37e

Implementing Adaptive Vectorial Centroid in Bayesian Logistic Regression for Interval Type-2 Fuzzy Sets [chapter]

Ku Muhammad Naim Ku Khalif, Alexander Gegov
2016 Studies in Computational Intelligence  
Propelled by such real applications, in this research study, the theoretical foundations of Vectorial Centroid of interval type-2 fuzzy set with Bayesian logistic regression is introduced.  ...  It additionally highlights the association of fuzzy sets with Bayesian logistic regression permits the use of fuzzy attributes by considering the need of human intuition in data analysis.  ...  Prior knowledge can be amalgamated into Bayesian logistic regression and the method is computationally efficient.  ... 
doi:10.1007/978-3-319-48506-5_16 fatcat:kg3on3pxanbxbaidutcxs3stjm

An adaptive MCMC method for Bayesian variable selection in logistic and accelerated failure time regression models

Kitty Yuen Yi Wan, Jim E. Griffin
2021 Statistics and computing  
This paper looks at applying one of these algorithms (the adaptively scaled independence sampler) to logistic regression and accelerated failure time models.  ...  AbstractBayesian variable selection is an important method for discovering variables which are most useful for explaining the variation in a response.  ...  Computational approaches In this section, we will concentrate on computational methods for Bayesian variable selection in two generalized linear models: logistic regression (for binary and some ordinal  ... 
doi:10.1007/s11222-020-09974-2 fatcat:xequbxteo5a2hokbwff7jsq6oi

A New Algorithm of Ensemble Learning for Medical Knowledge-Based Systems and Knowledge-Based Systems: Hybrid Bayesian Computing (Multinomial Logistic Regression Case-Based C5.0-Mixed Classification and Regression Tree)

Patcharaporn Paokanta, Somdet Srichairatanakool
2015 International Journal of Innovative Computing, Information and Control  
doi:10.24507/ijicic.11.03.965 fatcat:7ppyw64nqbcihgxunl6d6p3pvy

Using the EM algorithm for Bayesian variable selection in logistic regression models with related covariates

M. D. Koslovsky, M. D. Swartz, L. Leon-Novelo, W. Chan, A. V. Wilkinson
2017 Journal of Statistical Computation and Simulation  
We develop a Bayesian variable selection method for logistic regression models that can simultaneously accommodate qualitative covariates and interaction terms under various heredity constraints.  ...  We propose a variance adjustment of the priors for the coefficients of qualitative covariates, which controls false-positive rates, and a flexible parameterization for interaction terms, which accommodates  ...  Acknowledgments The authors would like to thank Dr Aubree Shay, UT School of Public Health-San Antonio Regional Campus, for her editorial support throughout the writing of this manuscript. Funding  ... 
doi:10.1080/00949655.2017.1398255 pmid:29731525 pmcid:PMC5935273 fatcat:wih4izwzazdydntn2unqlh7spi

Bayesian inference of the cumulative logistic principal component regression models

Minjung Kyung
2022 Communications for Statistical Applications and Methods  
We propose a Bayesian approach to cumulative logistic regression model for the ordinal response based on the orthogonal principal components via singular value decomposition considering the multicollinearity  ...  Also, we fit the suggested method to a real data concerning sprout-and scab-damaged kernels of wheat and compare it to EM based proportional-odds logistic regression model.  ...  Acknowledgement The authors would like to thank the Editor-in-Chief, the Associate Editor, and anonymous reviewers for their thoughtful and constructive comments on prior versions of this manuscript.  ... 
doi:10.29220/csam.2022.29.2.203 fatcat:kuga7gjewbgpbkzwvpzk5tns6m

Comparison of The Data-Mining Methods in Predicting The Risk Level of Diabetes

Andri Permana Wicaksono, Tessy Badriyah, Achmad Basuki
2016 Emitter: International Journal of Engineering Technology  
On the experiment result, it showed that two methods, Regression Logistic method and Bayesian method, have different performance excess score and are good at both.  ...  The purpose of this research is to compare the two methods in The data-mining, those are a Regression Logistic method and a Bayesian method, to predict the risk level of diabetes by web-based application  ...  Those data would be calculated by using Logistic Regression and Bayesian method. The computation result of Logistic Regression and Bayesian method then is used for analysis to predict diabetes.  ... 
doi:10.24003/emitter.v4i1.119 fatcat:uhnc4ycjqncwlmzsb3vlawvnsu

Logistic Regression Models for a Fast CBIR Method Based on Feature Selection

Riadh Ksantini, Djemel Ziou, Bernard Colin, François Dubeau
2007 International Joint Conference on Artificial Intelligence  
In this paper, we use a Bayesian logistic regression model, in order to compute the weights of a pseudo-metric to improve its discriminatory capacity and then to increase image retrieval accuracy.  ...  The Bayesian logistic regression model was shown to be a significantly better tool than the classical logistic regression one to improve the retrieval performance.  ...  computed by the Bayesian logistic regression model.  ... 
dblp:conf/ijcai/KsantiniZCD07 fatcat:3h2bxw4nubdf3azy77hktx26r4

Bayesian Logistic Regression Modelling via Markov Chain Monte Carlo Algorithm

Henry De-Graft Acquah
2013 Journal of Social and Development Sciences  
It is concluded that Bayesian Markov Chain Monte Carlo algorithm offers an alternative framework for estimating the logistic regression model.  ...  The Bayesian logistic regression estimation is compared with the classical logistic regression.  ...  The application of the logistic regression to binary response data is simple to understand, easy to compute and widely used.  ... 
doi:10.22610/jsds.v4i4.751 fatcat:2ygymmqkz5b57nkdrt4zjgr7zq

Aligning Bayesian Network Classifiers with Medical Contexts [chapter]

Linda C. van der Gaag, Silja Renooij, Ad Feelders, Arend de Groote, Marinus J. C. Eijkemans, Frank J. Broekmans, Bart C. J. M. Fauser
2009 Lecture Notes in Computer Science  
While for many problems in medicine classification models are being developed, Bayesian network classifiers do not seem to have become as widely accepted within the medical community as logistic regression  ...  We compare first-order logistic regression and naive Bayesian classification in the domain of reproductive medicine and demonstrate that the two techniques can result in models of comparable performance  ...  A logistic regression model provides for directly computing the posterior probabilities Pr(Y | x) for the class variable given an instance x, by filling in the values for the feature variables.  ... 
doi:10.1007/978-3-642-03070-3_59 fatcat:k3zy6k3oyrhxdplfokrjrnxpca

Integral approximations for computing optimum designs in random effects logistic regression models

C. Tommasi, J.M. Rodríguez-Díaz, M.T. Santos-Martín
2014 Computational Statistics & Data Analysis  
Locally D-, A-, coptimum designs and the optimum design to estimate a percentile are computed for the univariate logistic regression model with Gaussian random effects.  ...  Since locally optimum designs depend on a chosen nominal value for the parameter vector, a Bayesian D-optimum design is also computed.  ...  Locally D-, A-, coptimum designs and the optimum design to estimate a percentile are computed for the univariate logistic regression model with Gaussian random effects.  ... 
doi:10.1016/j.csda.2012.05.024 fatcat:hyrdiqjwnjgjnery3oesybhzee
« Previous Showing results 1 — 15 out of 55,741 results