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dynesty: A Dynamic Nested Sampling Package for Estimating Bayesian Posteriors and Evidences

Mixed citation behavior. Most common role is background (61%).

28 Pith papers citing it
Background 61% of classified citations
abstract

We present dynesty, a public, open-source, Python package to estimate Bayesian posteriors and evidences (marginal likelihoods) using Dynamic Nested Sampling. By adaptively allocating samples based on posterior structure, Dynamic Nested Sampling has the benefits of Markov Chain Monte Carlo algorithms that focus exclusively on posterior estimation while retaining Nested Sampling's ability to estimate evidences and sample from complex, multi-modal distributions. We provide an overview of Nested Sampling, its extension to Dynamic Nested Sampling, the algorithmic challenges involved, and the various approaches taken to solve them. We then examine dynesty's performance on a variety of toy problems along with several astronomical applications. We find in particular problems dynesty can provide substantial improvements in sampling efficiency compared to popular MCMC approaches in the astronomical literature. More detailed statistical results related to Nested Sampling are also included in the Appendix.

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Measurement prospects for the pair-instability mass cutoff with gravitational waves

astro-ph.HE · 2026-02-11 · conditional · novelty 6.0

Simulations show a 40-50 solar-mass black-hole cutoff is not guaranteed to be confidently recovered from GWTC-4-like catalogs, spurious detections are unlikely, and O4 data would reduce cutoff-mass uncertainty by at least 20 percent while yielding only a lower bound on the carbon-alpha reaction rate

The Impact of Spin Priors on Parameterized Tests of General Relativity

gr-qc · 2026-05-06 · unverdicted · novelty 5.0

Spin prior choices propagate into tests of GR via the 1.5PN deviation parameter δφ̂3 in a non-trivial, event-dependent way, with stronger effects for short-inspiral events and partial degeneracy with χ_eff when the deviation is included.

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