clusterBMA: Bayesian model averaging for clustering [PDF]
PLoS ONE, 2023 Various methods have been developed to combine inference across multiple sets of results for unsupervised clustering, within the ensemble clustering literature.
Owen Forbes +9 more
doaj +7 more sources
Bayesian model averaging: improved variable selection for matched case-control studies [PDF]
Epidemiology, Biostatistics and Public Health, 2019 Background: The problem of variable selection for risk factor modeling is an ongoing challenge in statistical practice. Classical methods that select one subset of exploratory risk factors dominate the medical research field.
Yi Mu +2 more
doaj +3 more sources
Bayesian model averaging for nonparametric discontinuity design. [PDF]
PLoS ONE, 2022 Quasi-experimental research designs, such as regression discontinuity and interrupted time series, allow for causal inference in the absence of a randomized controlled trial, at the cost of additional assumptions.
Max Hinne +3 more
doaj +6 more sources
A Conceptual Introduction to Bayesian Model Averaging [PDF]
Advances in Methods and Practices in Psychological Science, 2019 Many statistical scenarios initially involve several candidate models that describe the data-generating process. Analysis often proceeds by first selecting the best model according to some criterion and then learning about the parameters of this selected
M. Hinne +3 more
semanticscholar +7 more sources
Bayesian Additive Regression Trees using Bayesian Model Averaging. [PDF]
Stat Comput, 2018 Bayesian Additive Regression Trees (BART) is a statistical sum of trees model. It can be considered a Bayesian version of machine learning tree ensemble methods where the individual trees are the base learners. However for data sets where the number of variables $p$ is large (e.g.
Hernández B +3 more
europepmc +6 more sources
Accurate phenotyping: Reconciling approaches through Bayesian model averaging. [PDF]
PLoS ONE, 2017 Genetic research into complex diseases is frequently hindered by a lack of clear biomarkers for phenotype ascertainment. Phenotypes for such diseases are often identified on the basis of clinically defined criteria; however such criteria may not be ...
Carla Chia-Ming Chen +2 more
doaj +2 more sources
OBAMA: OBAMA for Bayesian amino-acid model averaging [PDF]
PeerJ, 2020 Background Bayesian analyses offer many benefits for phylogenetic, and have been popular for analysis of amino acid alignments. It is necessary to specify a substitution and site model for such analyses, and often an ad hoc, or likelihood based method is
Remco R. Bouckaert
doaj +6 more sources
Bayesian Network Model Averaging Classifiers by Subbagging
Entropy, 2022 When applied to classification problems, Bayesian networks are often used to infer a class variable when given feature variables. Earlier reports have described that the classification accuracy of Bayesian network structures achieved by maximizing the ...
Shouta Sugahara, Itsuki Aomi, Maomi Ueno
doaj +3 more sources
Bayesian Model Averaging with the Integrated Nested Laplace Approximation [PDF]
Econometrics, 2020 The integrated nested Laplace approximation (INLA) for Bayesian inference is an efficient approach to estimate the posterior marginal distributions of the parameters and latent effects of Bayesian hierarchical models that can be expressed as latent ...
Virgilio Gómez-Rubio +2 more
doaj +2 more sources
The simple multivariable model for predicting liver fibrosis in Vietnamese male adults: a combination of Bayesian model averaging and stepwise method [PDF]
PeerJBackground Liver fibrosis is a significant health burden in Vietnamese male adults, driven by high rates of hepatitis B and hepatitis C, excessive alcohol consumption, and genetic and environmental factors.
Nghia Nhu Nguyen +9 more
doaj +3 more sources