Dynamic nested sampling: an improved algorithm for parameter estimation and evidence calculation
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Abstract (arXiv)
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 empirical tests the new method significantly improves calculation accuracy compared to standard nested sampling with the same number of samples; this increase in accuracy is equivalent to speeding up the computation by factors of up to ~72 for parameter estimation and ~7 for evidence calculations. We also show that the accuracy of both parameter estimation and evidence calculations can be improved simultaneously. In addition, unlike in standard nested sampling, more accurate results can be obtained by continuing the calculation for longer. Popular standard nested sampling implementations can be easily adapted to perform dynamic nested sampling, and several dynamic nested sampling software packages are now publicly available.
Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Apr 2017.
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Citing papers per year: 2021: 3; 2022: 4; 2023: 2; 2024: 4; 2025: 1; 2026: 1.
15 papers.
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- 2021Truncated Marginal Neural Ratio Estimationcited by 30
- 2022Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel Recombinationcited by 20
- 2023Improving Gradient-guided Nested Sampling for Posterior Inferencecited by 14
- 2021Fast and Credible Likelihood-Free Cosmology with Truncated Marginal Neural Ratio Estimationcited by 13
- 2021Arbitrary Marginal Neural Ratio Estimation for Simulation-based Inferencecited by 5
- 2022Fast and robust Bayesian Inference using Gaussian Processes with GPrycited by 5
- 2022A stochastic Stein Variational Newton methodcited by 4
- 2024Bayesian evidence estimation from posterior samples with normalizing flowscited by 4
- 2024Flow Matching for Atmospheric Retrieval of Exoplanets: Where Reliability meets Adaptive Noise Levelscited by 2
- 2022Active Nearest Neighbor Regression Through Delaunay Refinementcited by 1
- 2025Diffusion-based supervised learning of generative models for efficient sampling of multimodal distributionscited by 1
- 2023Parallelized Acquisition for Active Learning using Monte Carlo Samplingcited by 0
3 more
- 2026Nested Sampling: A Critical and Comprehensive Theoretical Guide16 Jun 2026
- 2025Diffusion-based supervised learning of generative models for efficient sampling of multimodal distributions20 Apr 2025
- 2024Flow Matching for Atmospheric Retrieval of Exoplanets: Where Reliability meets Adaptive Noise Levels28 Oct 2024
- 2024A comparison of Bayesian sampling algorithms for high-dimensional particle physics and cosmology applications27 Sep 2024
- 2024Bayesian evidence estimation from posterior samples with normalizing flows18 Apr 2024
- 2024On the Laplace Approximation as Model Selection Criterion for Gaussian Processes14 Mar 2024
- 2023Improving Gradient-guided Nested Sampling for Posterior Inference6 Dec 2023
- 2023Parallelized Acquisition for Active Learning using Monte Carlo Sampling30 May 2023
- 2022Fast and robust Bayesian Inference using Gaussian Processes with GPry3 Nov 2022
- 2022Active Nearest Neighbor Regression Through Delaunay Refinement16 Jun 2022
- 2022Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel Recombination9 Jun 2022
- 2022A stochastic Stein Variational Newton method19 Apr 2022
- 2021Truncated Marginal Neural Ratio Estimationcited by 30
- 2022Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel Recombinationcited by 20
- 2023Improving Gradient-guided Nested Sampling for Posterior Inferencecited by 14
- 2021Fast and Credible Likelihood-Free Cosmology with Truncated Marginal Neural Ratio Estimationcited by 13
- 2021Arbitrary Marginal Neural Ratio Estimation for Simulation-based Inferencecited by 5
- 2022Fast and robust Bayesian Inference using Gaussian Processes with GPrycited by 5
- 2022A stochastic Stein Variational Newton methodcited by 4
- 2024Bayesian evidence estimation from posterior samples with normalizing flowscited by 4
- 2024Flow Matching for Atmospheric Retrieval of Exoplanets: Where Reliability meets Adaptive Noise Levelscited by 2
- 2022Active Nearest Neighbor Regression Through Delaunay Refinementcited by 1
- 2025Diffusion-based supervised learning of generative models for efficient sampling of multimodal distributionscited by 1
- 2023Parallelized Acquisition for Active Learning using Monte Carlo Samplingcited by 0
12 most cited of 15 All 15 (JSON)
- 2026Nested Sampling: A Critical and Comprehensive Theoretical Guide16 Jun 2026
- 2025Diffusion-based supervised learning of generative models for efficient sampling of multimodal distributions20 Apr 2025
- 2024Flow Matching for Atmospheric Retrieval of Exoplanets: Where Reliability meets Adaptive Noise Levels28 Oct 2024
- 2024A comparison of Bayesian sampling algorithms for high-dimensional particle physics and cosmology applications27 Sep 2024
- 2024Bayesian evidence estimation from posterior samples with normalizing flows18 Apr 2024
- 2024On the Laplace Approximation as Model Selection Criterion for Gaussian Processes14 Mar 2024
- 2023Improving Gradient-guided Nested Sampling for Posterior Inference6 Dec 2023
- 2023Parallelized Acquisition for Active Learning using Monte Carlo Sampling30 May 2023
- 2022Fast and robust Bayesian Inference using Gaussian Processes with GPry3 Nov 2022
- 2022Active Nearest Neighbor Regression Through Delaunay Refinement16 Jun 2022
- 2022Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel Recombination9 Jun 2022
- 2022A stochastic Stein Variational Newton method19 Apr 2022
12 newest of 15 All 15 (JSON)