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| Inferenza Bayesiana Gerarchica× | Hierarchical Markov Chain Monte Carlo× | |
|---|---|---|
| Campo | Bayesiano | Bayesiano |
| Famiglia | Bayesian methods | Bayesian methods |
| Anno di origine≠ | 1972 (Lindley & Smith); consolidated 1995–2013 | 1990 |
| Ideatore≠ | Lindley & Smith; Gelman et al. | Gelfand & Smith (1990), building on Geman & Geman (1984) |
| Tipo≠ | Bayesian multilevel model | Bayesian computational sampler |
| Fonte seminale | 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., 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 |
| Alias | multilevel Bayesian modeling, Bayesian hierarchical model, nested Bayesian model, partial pooling model | hierarchical MCMC, MCMC for multilevel models, Bayesian hierarchical MCMC, multilevel MCMC sampling |
| Correlati | 6 | 6 |
| Sintesi≠ | 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. | Hierarchical Markov chain Monte Carlo applies MCMC sampling to hierarchical Bayesian models, jointly drawing from the posterior over both observation-level parameters and the hyperparameters that govern them. This allows principled uncertainty propagation across all levels of a multilevel structure, from individuals to groups to population, using algorithms such as Gibbs sampling, Metropolis-Hastings, or Hamiltonian Monte Carlo. |
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