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Markov chain Monte Carlo without likelihoods
Many stochastic simulation approaches for generating observations from a posterior distribution depend on knowing a likelihood function. However, for many complex probability models, such likelihoods are either impossible or computationally prohibitive to obtain. Here we present a Markov chain Monte...
Tallennettuna:
| Julkaisussa: | Proc Natl Acad Sci U S A |
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| Päätekijät: | , , , |
| Aineistotyyppi: | Artigo |
| Kieli: | Inglês |
| Julkaistu: |
National Academy of Sciences
2003
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| Aiheet: | |
| Linkit: | https://ncbi.nlm.nih.govhttps://pmc.ncbi.nlm.nih.gov/articles/PMC307566/ https://ncbi.nlm.nih.govhttps://pubmed.ncbi.nlm.nih.gov/14663152/ https://ncbi.nlm.nih.govhttps://doi.org/10.1073/pnas.0306899100 |
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