Cargando…
AKL-ABC: An Automatic Approximate Bayesian Computation Approach Based on Kernel Learning
Bayesian statistical inference under unknown or hard to asses likelihood functions is a very challenging task. Currently, approximate Bayesian computation (ABC) techniques have emerged as a widely used set of likelihood-free methods. A vast number of ABC-based approaches have appeared in the literat...
Autores principales: | González-Vanegas, Wilson, Álvarez-Meza, Andrés, Hernández-Muriel, José, Orozco-Gutiérrez, Álvaro |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514265/ http://dx.doi.org/10.3390/e21100932 |
Ejemplares similares
-
Emergent computation: a festschrift for Selim G. Akl
por: Adamatzky, Andrew
Publicado: (2017) -
ABC-SysBio—approximate Bayesian computation in Python with GPU support
por: Liepe, Juliane, et al.
Publicado: (2010) -
Kernel-Based Relevance Analysis with Enhanced Interpretability for Detection of Brain Activity Patterns
por: Alvarez-Meza, Andres M., et al.
Publicado: (2017) -
GpABC: a Julia package for approximate Bayesian computation with Gaussian process emulation
por: Tankhilevich, Evgeny, et al.
Publicado: (2020) -
Kernel-density estimation and approximate Bayesian computation for flexible epidemiological model fitting in Python
por: Irvine, Michael A., et al.
Publicado: (2018)