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Found 5 papers, 3 papers with code
Bayesian posterior repartitioning for nested sampling
We show through numerical examples that this Bayesian PR (BPR) method provides a very robust, self-adapting and computationally efficient `hands-off' solution to the problem of unrepresentative priors in Bayesian inference using NS.
Robust parameter estimation for compact binaries with ground-based gravitational-wave observations using the LALInference software library
We show that our implementation is able to correctly recover the parameters of compact binary signals from simulated data from the advanced GW detectors.
General Relativity and Quantum Cosmology High Energy Astrophysical Phenomena Instrumentation and Methods for Astrophysics
SKYNET: an efficient and robust neural network training tool for machine learning in astronomy
We present the first public release of our generic neural network training algorithm, called SkyNet.
BAMBI: blind accelerated multimodal Bayesian inference
In this paper we present an algorithm for rapid Bayesian analysis that combines the benefits of nested sampling and artificial neural networks.
Multimodal nested sampling: an efficient and robust alternative to MCMC methods for astronomical data analysis
Shaw et al. (2007), recently introduced a clustered nested sampling method which is significantly more efficient in sampling from multimodal posteriors and also determines the expectation and variance of the final evidence from a single run of the algorithm, hence providing a further increase in efficiency.
