Dirichlet Process Mixture Model
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.
Rekodi ya chanzo
Nukuu zimehamishwa kwa uhalisi kutoka kwa rekodi ya chanzo cha mbinu. Hakuna uthibitisho wa kiwango cha dai unaodokezwa kutoka kwao.
- Ferguson, T. S. (1973). A Bayesian analysis of some nonparametric problems. The Annals of Statistics, 1(2), 209–230. · DOI 10.1214/aos/1176342360
- Neal, R. M. (2000). Markov chain sampling methods for Dirichlet process mixture models. Journal of Computational and Graphical Statistics, 9(2), 249–265. · DOI 10.1080/10618600.2000.10474879
- Hjort, N. L., Holmes, C., Müller, P., & Walker, S. G. (Eds.) (2010). Bayesian Nonparametrics. Cambridge University Press. · ISBN 978-0-521-51346-3
Madai yaliyotunzwa
Madai yamehifadhiwa katika daftari la ushahidi, kila moja ikiwa na tathmini yake.
Mwonekano huu haubuni tathmini ya dai wakati daftari haina yoyote.
Mbinu zinazohusiana
Zilizotengenezwa kutoka kwa grafu ya mbinu na kuonyeshwa kama uhusiano uliopendekezwa na mashine — hakuna dai la ushahidi linalodokezwa.