FiEstAS sampling -- a Monte Carlo algorithm for multidimensional numerical integration
arXiv:0807.4479 · doi:10.1016/j.cpc.2008.07.011
Abstract
This paper describes a new algorithm for Monte Carlo integration, based on the Field Estimator for Arbitrary Spaces (FiEstAS). The algorithm is discussed in detail, and its performance is evaluated in the context of Bayesian analysis, with emphasis on multimodal distributions with strong parameter degeneracies. Source code is available upon request.
18 pages, 3 figures, submitted to Comp. Phys. Comm
References in corpus (6)
- Multimodal nested sampling: an efficient and robust alternative to MCMC methods for astronomical data analysis
- Cuba - a library for multidimensional numerical integration
- Efficient Bayesian inference for multimodal problems in cosmology
- Numerical estimation of densities
- Multi-Dimensional Density Estimation and Phase Space Structure Of Dark Matter Halos
- Sampling using a `bank' of clues
Cited by in corpus (4)
- Do galaxies form a spectroscopic sequence?
- Estimating multidimensional probability fields using the Field Estimator for Arbitrary Spaces (FiEstAS) with applications to astrophysics
- The contribution of star-forming galaxies to the cosmic radio background
- Hydrostatic photoionization models of the Orion Bar