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| Modelo de Mezcla de Procesos de Dirichlet× | Cadenas de Markov Monte Carlo (MCMC)× | |
|---|---|---|
| Campo | Bayesiano | Bayesiano |
| Familia | Bayesian methods | Bayesian methods |
| Año de origen≠ | 1973 | — |
| Autor original≠ | Ferguson (1973); mixture model formulation by Lo (1984) | — |
| Tipo≠ | Nonparametric Bayesian mixture model | Posterior sampling algorithm |
| Fuente seminal≠ | Ferguson, T. S. (1973). A Bayesian analysis of some nonparametric problems. The Annals of Statistics, 1(2), 209–230. 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 |
| Alias≠ | DPMM, DP mixture model, infinite mixture model, Dirichlet process mixture | markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo) |
| Relacionados | 3 | 3 |
| Resumen≠ | The Dirichlet Process Mixture Model (DPMM) is a nonparametric Bayesian clustering method introduced through Ferguson's (1973) Dirichlet process prior that places a probability distribution over distributions. Unlike finite mixture models, the DPMM does not require the analyst to specify the number of clusters in advance; instead it infers the number of components from the data, allowing an effectively unbounded mixture that grows as more observations arrive. | Markov Chain Monte Carlo (MCMC) is a family of computational algorithms for sampling from complex probability distributions, most commonly the posterior distributions that arise in Bayesian inference. Rather than computing posteriors analytically — which is rarely possible for realistic models — MCMC constructs a Markov chain whose stationary distribution is the target posterior and draws dependent samples from it, enabling full probabilistic inference for virtually any model. |
| ScholarGateConjunto de datos ↗ |
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