Sammenlign metoder
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| Hierarkisk Approksimativ Bayesiansk Beregning× | Hierarkisk Markov Chain Monte Carlo× | |
|---|---|---|
| Fagområde | Bayesiansk | Bayesiansk |
| Familie | Bayesian methods | Bayesian methods |
| Oprindelsesår≠ | 2009–2010 | 1990 |
| Ophavsperson≠ | Toni, Welch, Strelkowa, Ipsen & Stumpf (building on Pritchard et al. 1999 and Beaumont et al. 2002) | Gelfand & Smith (1990), building on Geman & Geman (1984) |
| Type≠ | simulation-based Bayesian inference | Bayesian computational sampler |
| Oprindelig kilde≠ | Toni, T. & Stumpf, M. P. H. (2010). Simulation-based model selection for dynamical systems in systems and population biology. Bioinformatics, 26(1), 104–110. DOI ↗ | 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 |
| Aliasser | hierarchical ABC, ABC for hierarchical models, multilevel ABC, population ABC | hierarchical MCMC, MCMC for multilevel models, Bayesian hierarchical MCMC, multilevel MCMC sampling |
| Relaterede≠ | 4 | 6 |
| Resumé≠ | Hierarchical ABC is a likelihood-free Bayesian inference method designed for multilevel data structures in which individual-level parameters are themselves drawn from a population-level distribution. By combining simulation-based rejection sampling with hierarchical pooling, it recovers both within-group and between-group posterior distributions without requiring a tractable likelihood function. | 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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