Cargando…
PHYSTAT Seminar: Accelerating Bayesian Computation: Parallelizing Markov Chain Monte Carlo
<!--HTML--><div> <div> <div> <p>A full-fledged Bayesian computation requries evaluation of the posterior probability density in the complete parameter space. This can become very time consuming using commonly used algorithms such as Markov Chain Monte Carlos....
Autor principal: | Caldwell, Allen |
---|---|
Lenguaje: | eng |
Publicado: |
2020
|
Materias: | |
Acceso en línea: | http://cds.cern.ch/record/2706495 |
Ejemplares similares
-
PHYSTAT Seminar: A general introduction to continuous optimization
por: Mueller, Christian
Publicado: (2019) -
PHYSTAT seminar: On relating Uncertainties in Machine Learning and HEP
por: Kagan, Michael
Publicado: (2022) -
PHYSTAT seminar: From COVID-19 Testing to Election Prediction: How Small Are Our Big Data?
por: Meng, Xiao-Li
Publicado: (2021) -
Massively parallel Markov chain Monte Carlo with BAT
por: Beaujean, Frederik
Publicado: (2012) -
Parallel Markov chain Monte Carlo - bridging the gap to high-performance Bayesian computation in animal breeding and genetics
por: Wu, Xiao-Lin, et al.
Publicado: (2012)