Cargando…
Selecting Summary Statistics in Approximate Bayesian Computation for Calibrating Stochastic Models
Approximate Bayesian computation (ABC) is an approach for using measurement data to calibrate stochastic computer models, which are common in biology applications. ABC is becoming the “go-to” option when the data and/or parameter dimension is large because it relies on user-chosen summary statistics...
Autores principales: | Burr, Tom, Skurikhin, Alexei |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2013
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3830866/ https://www.ncbi.nlm.nih.gov/pubmed/24288668 http://dx.doi.org/10.1155/2013/210646 |
Ejemplares similares
-
An automatic adaptive method to combine summary statistics in approximate Bayesian computation
por: Harrison, Jonathan U., et al.
Publicado: (2020) -
Informative and adaptive distances and summary statistics in sequential approximate Bayesian computation
por: Schälte, Yannik, et al.
Publicado: (2023) -
A Novel Approach for Choosing Summary Statistics in Approximate Bayesian Computation
por: Aeschbacher, Simon, et al.
Publicado: (2012) -
Personalized pathology test for Cardio-vascular disease: Approximate Bayesian computation with discriminative summary statistics learning
por: Dutta, Ritabrata, et al.
Publicado: (2022) -
Dynamic calibration with approximate Bayesian computation for a microsimulation of disease spread
por: Asher, Molly, et al.
Publicado: (2023)