Bayesian methodsBayesian / computational
Hierarchical Bayesian Inference
Hierarchical Bayesian inference is a probabilistic modeling framework that organises parameters into levels, placing priors on the group-level parameters and hyperpriors on the parameters governing those priors. It enables partial pooling of information across groups, balancing the extremes of treating each group as independent or merging them into a single estimate.
MethodMind'de açSoonVideoSoon
Tam yöntemi oku
Members only
Sign inSign in with a free account to read this section.
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
- Gelman, A. (2006). Multilevel (hierarchical) modeling: what it can and cannot do. Technometrics, 48(3), 432-435. DOI: 10.1198/004017005000000661 ↗
Related methods
Referenced by
Bayesian Hierarchical Model with Missing DataBayesian Inference with Measurement ErrorBayesian Inference with Missing DataDynamic Bayesian Hierarchical ModelDynamic Bayesian InferenceDynamic Bayesian NetworkGibbs SamplingHierarchical Approximate Bayesian ComputationHierarchical Bayesian Model AveragingHierarchical Bayesian NetworkHierarchical Bootstrap SimulationHierarchical Hamiltonian Monte CarloHierarchical Kalman FilterHierarchical Markov Chain Monte CarloHierarchical Particle FilterHierarchical Variational InferenceMCMC with Measurement ErrorMultilevel Approximate Bayesian ComputationMultilevel Bayesian InferenceMultilevel Bayesian Model AveragingMultilevel Bayesian NetworkMultilevel Bootstrap SimulationMultilevel Gibbs SamplingMultilevel MCMCMultilevel Metropolis-HastingsMultilevel Variational InferenceRobust Bayesian InferenceRobust Bayesian Model AveragingRobust Bayesian NetworkSpatial Bayesian InferenceSpatial Bayesian Model AveragingSpatial MCMCTime series Bayesian hierarchical modelTime series Bayesian inference