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

Related methods

MCMC OLS Regression

Referenced by

Automatic Differentiation Variational Inference Bayes Factor Test Bayesian ANOVA Bayesian Factor Analysis Bayesian Hierarchical Model Bayesian Inference with Measurement Error Bayesian Inference with Missing Data Bayesian Instrumental Variables Bayesian Linear Regression Bayesian Logistic Regression Bayesian Model Averaging Bayesian Model Averaging with Measurement Error Bayesian Network Bayesian Nonparametric Methods Bayesian SEM Bayesian Structural Time Series Bayesian Survival Analysis Bayesian t-Test Conjugate Prior Analysis Differential Evolution Dirichlet Process Mixture Model Dynamic Bayesian Inference Dynamic Hamiltonian Monte Carlo Empirical Bayes Gibbs Sampling Hamiltonian Monte Carlo Hierarchical Bayesian Inference Hierarchical Bayesian Model Averaging Hierarchical Hamiltonian Monte Carlo Hierarchical Markov Chain Monte Carlo Hierarchical Variational Inference Kalman Filter Laplace Approximation Markov Chain Monte Carlo MCMC MCMC with Measurement Error Metropolis-Hastings Algorithm Mixed Logit Multilevel Bayesian Inference Multilevel Bayesian Model Averaging Multilevel MCMC No-U-Turn Sampler Particle Filter Robust Bayesian Inference Robust Bayesian Model Averaging Robust Gibbs Sampling Robust Variational Inference Slice Sampling Spatial Bayesian Model Averaging Time series Bayesian hierarchical model Time series Bayesian inference Time series Bayesian model averaging Time Series Kalman Filter Variational Inference

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