Cargando…
Likelihood-free inference via classification
Increasingly complex generative models are being used across disciplines as they allow for realistic characterization of data, but a common difficulty with them is the prohibitively large computational cost to evaluate the likelihood function and thus to perform likelihood-based statistical inferenc...
Autores principales: | Gutmann, Michael U., Dutta, Ritabrata, Kaski, Samuel, Corander, Jukka |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6956883/ https://www.ncbi.nlm.nih.gov/pubmed/31997856 http://dx.doi.org/10.1007/s11222-017-9738-6 |
Ejemplares similares
-
Fundamentals and Recent Developments in Approximate Bayesian Computation
por: Lintusaari, Jarno, et al.
Publicado: (2017) -
PYLFIRE: Python implementation of likelihood-free inference by ratio estimation
por: Kokko, Jan, et al.
Publicado: (2019) -
Resolving outbreak dynamics using approximate Bayesian computation for stochastic birth–death models
por: Lintusaari, Jarno, et al.
Publicado: (2019) -
Machine Learning for Likelihood Free Inference
por: Le Pottier, Luc Tomas
Publicado: (2019) -
On predictive inference for intractable models via approximate Bayesian computation
por: Järvenpää, Marko, et al.
Publicado: (2023)