Gradient-free MCMC methods for dynamic causal modelling

In this technical note we compare the performance of four gradient-free MCMC samplers (random walk Metropolis sampling, slice-sampling, adaptive MCMC sampling and population-based MCMC sampling with tempering) in terms of the number of independent samples they can produce per unit computational time...

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Detalles Bibliográficos
Autores principales: Sengupta, Biswa, Friston, Karl J., Penny, Will D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Academic Press 2015
Materias:
Technical Note
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4410946/
https://www.ncbi.nlm.nih.gov/pubmed/25776212
http://dx.doi.org/10.1016/j.neuroimage.2015.03.008

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4410946/
https://www.ncbi.nlm.nih.gov/pubmed/25776212
http://dx.doi.org/10.1016/j.neuroimage.2015.03.008

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