Porovnat metody

	Hierarchická Bayesovská síť	Hierarchické vzorkování Markovovým řetězcem Monte Carlo
Obor	Bayesovská statistika	Bayesovská statistika
Rodina	Bayesian methods	Bayesian methods
Rok vzniku	1990s–2000s	1990
Tvůrce	Koller, Friedman, and colleagues	Gelfand & Smith (1990), building on Geman & Geman (1984)
Typ	probabilistic graphical model	Bayesian computational sampler
Původní zdroj	Koller, D. & Friedman, N. (2009). Probabilistic Graphical Models: Principles and Techniques. MIT Press. ISBN: 978-0262013192	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
Další názvy	HBN, layered Bayesian network, multi-level Bayesian network, hierarchical probabilistic graphical model	hierarchical MCMC, MCMC for multilevel models, Bayesian hierarchical MCMC, multilevel MCMC sampling
Příbuzné	6	6
Shrnutí	A hierarchical Bayesian network is a probabilistic graphical model that organizes variables across multiple levels of abstraction. Higher-level nodes govern the prior distributions of lower-level nodes through hyperparameters, enabling structured sharing of information across groups, contexts, or data subsets while preserving the directed acyclic graph (DAG) representation of conditional dependencies.	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.