Bayesian methods
Bayesian Regression
Bayesian regression is a probabilistic version of linear regression that treats the model parameters as uncertain quantities. Instead of returning a single best-fit estimate, it combines prior knowledge with the observed data to produce a full posterior probability distribution for each parameter, from which credible intervals and predictions are read off.
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Sources
- Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955
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Referenced by
Automatic Differentiation Variational InferenceBayes Factor TestBayesian ANOVABayesian Factor AnalysisBayesian Hierarchical ModelBayesian Inference with Measurement ErrorBayesian Inference with Missing DataBayesian Instrumental VariablesBayesian Linear RegressionBayesian Logistic RegressionBayesian Model AveragingBayesian Model Averaging with Measurement ErrorBayesian NetworkBayesian Nonparametric MethodsBayesian SEMBayesian Structural Time SeriesBayesian Survival AnalysisBayesian t-TestConjugate Prior AnalysisDifferential EvolutionDirichlet Process Mixture ModelDynamic Bayesian InferenceDynamic Hamiltonian Monte CarloEmpirical BayesGibbs SamplingHamiltonian Monte CarloHierarchical Bayesian InferenceHierarchical Bayesian Model AveragingHierarchical Hamiltonian Monte CarloHierarchical Markov Chain Monte CarloHierarchical Variational InferenceKalman FilterLaplace ApproximationMarkov Chain Monte CarloMCMCMCMC with Measurement ErrorMetropolis-Hastings AlgorithmMixed LogitMultilevel Bayesian InferenceMultilevel Bayesian Model AveragingMultilevel MCMCNo-U-Turn SamplerParticle FilterRobust Bayesian InferenceRobust Bayesian Model AveragingRobust Gibbs SamplingRobust Variational InferenceSlice SamplingSpatial Bayesian Model AveragingTime series Bayesian hierarchical modelTime series Bayesian inferenceTime series Bayesian model averagingTime Series Kalman FilterVariational Inference