317 citations · 622 across the 6 of their papers we have counts for
6 papers
Nested sampling for physical scientists
Greg Ashton, Noam Bernstein, Johannes Buchner +20
We review Skilling's nested sampling (NS) algorithm for Bayesian inference and more broadly multi-dimensional integration. After recapitulating the principles of NS, we survey deve…
Bayesian sparse reconstruction: a brute-force approach to astronomical imaging and machine learning
Edward Higson, Will Handley, Michael Hobson +1
We present a principled Bayesian framework for signal reconstruction, in which the signal is modelled by basis functions whose number (and form, if required) is determined by the d…
Towards a framework for testing general relativity with extreme-mass-ratio-inspiral observations
Alvin J. K. Chua, Sonke Hee, Will J. Handley +5
Extreme-mass-ratio-inspiral observations from future space-based gravitational-wave detectors such as LISA will enable strong-field tests of general relativity with unprecedented p…
nestcheck: diagnostic tests for nested sampling calculations
Edward Higson, Will Handley, Mike Hobson +1
Nested sampling is an increasingly popular technique for Bayesian computation, in particular for multimodal, degenerate problems of moderate to high dimensionality. Without appropr…
Dynamic nested sampling: an improved algorithm for parameter estimation and evidence calculation
Edward Higson, Will Handley, Mike Hobson +1
We introduce dynamic nested sampling: a generalisation of the nested sampling algorithm in which the number of "live points" varies to allocate samples more efficiently. In empiric…
Sampling Errors in Nested Sampling Parameter Estimation
Edward Higson, Will Handley, Mike Hobson +1
Sampling errors in nested sampling parameter estimation differ from those in Bayesian evidence calculation, but have been little studied in the literature. This paper provides the…