Cargando…
Comparing Dynamic Causal Models using AIC, BIC and Free Energy
In neuroimaging it is now becoming standard practise to fit multiple models to data and compare them using a model selection criterion. This is especially prevalent in the analysis of brain connectivity. This paper describes a simulation study which compares the relative merits of three model select...
Autor principal: | Penny, W.D. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Academic Press
2012
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3200437/ https://www.ncbi.nlm.nih.gov/pubmed/21864690 http://dx.doi.org/10.1016/j.neuroimage.2011.07.039 |
Ejemplares similares
-
Gradient-free MCMC methods for dynamic causal modelling
por: Sengupta, Biswa, et al.
Publicado: (2015) -
Selecting high-dimensional mixed graphical models using minimal AIC or BIC forests
por: Edwards, David, et al.
Publicado: (2010) -
Efficient gradient computation for dynamical models
por: Sengupta, B., et al.
Publicado: (2014) -
Gradient-based MCMC samplers for dynamic causal modelling
por: Sengupta, Biswa, et al.
Publicado: (2016) -
Using Bonferroni, BIC and AIC to assess evidence for alternative biological pathways: covariate selection for the multilevel Embryo-Uterus model
por: Stylianou, Christos, et al.
Publicado: (2013)