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We present a performant, general-purpose gradient-guided nested sampling algorithm, ${\tt GGNS}$, combining the state of the art in differentiable programming, Hamiltonian slice sampling, clustering, mode separation, dynamic nested sampling, and parallelization.
Sequential monte carlo
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Hybrid monte carlo
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Novel approach to nonlinear/non-gaussian bayesian state estimation
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Comments on “representations of knowledge in complex systems” by u. grenander and mi miller
Julian Besag · 1994
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Exponential convergence of Langevin distributions and their discrete approximations
Gareth O Roberts and Richard L Tweedie · 1996
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Optimal scaling of discrete approximations to Langevin diffusions
Gareth O Roberts and Jeffrey S Rosenthal · 1998
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A sequential particle filter method for static models
Nicolas Chopin · 2002
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Information theory, inference and learning algorithms
David JC MacKay · 2003
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Slice sampling
Radford M Neal · 2003
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Sequential monte carlo samplers
Pierre Del Moral, Arnaud Doucet, and Ajay Jasra · 2006
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Bayesian evidence as a tool for comparing datasets
Phil Marshall, Nutan Rajguru, and Anže Slosar · 2006
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A nested sampling algorithm for cosmological model selection
Pia Mukherjee, David Parkinson, and Andrew R Liddle · 2006
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Nested sampling for general Bayesian computation
John Skilling · 2006
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Multimodal nested sampling: an efficient and robust alternative to Markov Chain Monte Carlo methods for astronomical data analyses
F. Feroz and M. P. Hobson · 2007
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MultiNest: an efficient and robust bayesian inference tool for cosmology and particle physics
Farhan Feroz, MP Hobson, and Michael Bridges · 2009
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Nested sampling with constrained Hamiltonian Monte Carlo
Michael Betancourt · 2011
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MCMC using Hamiltonian dynamics
Radford M Neal et al · 2011
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JAXNS: a high-performance nested sampling package based on JAX
Joshua G. Albert · 2012
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Exploring the energy landscapes of protein folding simulations with bayesian computation
Nikolas S Burkoff, Csilla Várnai, Stephen A Wells, and David L Wild · 2012
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Exploring multi-modal distributions with nested sampling
Farhan Feroz and John Skilling · 2013
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Nested sampling in the canonical ensemble: Direct calculation of the partition function from NVT trajectories
Steven O. Nielsen · 2013
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Parallelized nested sampling
R Wesley Henderson and Paul M Goggans · 2014
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The No-U-Turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo
Matthew D Hoffman, Andrew Gelman, et al · 2014
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Superposition enhanced nested sampling
Stefano Martiniani, Jacob D Stevenson, David J Wales, and Daan Frenkel · 2014
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Nested sampling with demons
Michael Habeck · 2015
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Reflection, refraction, and hamiltonian monte carlo
Hadi Mohasel Afshar and Justin Domke · 2015
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Slice sampling on hamiltonian trajectories
Benjamin Bloem-Reddy and John Cunningham · 2016
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DNest4: Diffusive Nested Sampling in C++ and Python
Brendon J. Brewer and Daniel Foreman-Mackey · 2016
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Flow network based generative models for non-iterative diverse candidate generation
Emmanuel Bengio, Moksh Jain, Maksym Korablyov, Doina Precup, and Yoshua Bengio · 2021
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UltraNest - a robust, general purpose Bayesian inference engine
Johannes Buchner · 2021
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Johannes Buchner · 2021
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allesfitter: Flexible star and exoplanet inference from photometry and radial velocity
Maximilian N Günther and Tansu Daylan · 2021
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Nested sampling for materials
Livia B. Pártay, Gábor Csányi, and Noam Bernstein · 2021
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Nested sampling with normalizing flows for gravitational-wave inference
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Towards unifying Hamiltonian Monte Carlo and slice sampling
Yizhe Zhang, Xiangyu Wang, Changyou Chen, Ricardo Henao, Kai Fan, and Lawrence Carin · 2016
Cited alongside, same era.
Constant-pressure nested sampling with atomistic dynamics
Robert J. N. Baldock, Noam Bernstein, K. Michael Salerno, Lívia B. Pártay, and Gábor Csányi · 2017
Cited alongside, same era.
Helios–retrieval: an open-source, nested sampling atmospheric retrieval code; application to the hr 8799 exoplanets and inferred constraints for planet formation
Baptiste Lavie, João M Mendonça, Christoph Mordasini, Matej Malik, Mickaël Bonnefoy, Brice-Olivier Demory, Maria Oreshenko, Simon L Grimm, David Ehrenreich, and Kevin Heng · 2017
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Model Selection and Parameter Inference in Phylogenetics Using Nested Sampling
Patricio Maturana Russel, Brendon J Brewer, Steffen Klaere, and Remco R Bouckaert · 2018
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Importance Nested Sampling and the MultiNest Algorithm
Farhan Feroz, Michael P. Hobson, Ewan Cameron, and Anthony N. Pettitt · 2019
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anesthetic: nested sampling visualisation
Will Handley · 2019
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Michael J. Williams, John Veitch, and Chris Messenger · 2021
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Posterior samples of source galaxies in strong gravitational lenses with score-based priors
Alexandre Adam, Adam Coogan, Nikolay Malkin, Ronan Legin, Laurence Perreault-Levasseur, Yashar Hezaveh, and Yoshua Bengio · 2022
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Nested sampling for physical scientists
Greg Ashton, Noam Bernstein, Johannes Buchner, Xi Chen, Gábor Csányi, Andrew Fowlie, Farhan Feroz, Matthew Griffiths, Will Handley, Michael Habeck, et al · 2022
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Proximal nested sampling for high-dimensional bayesian model selection
Xiaohao Cai, Jason D McEwen, and Marcelo Pereyra · 2022
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Trajectory balance: Improved credit assignment in GFlowNets
Nikolay Malkin, Moksh Jain, Emmanuel Bengio, Chen Sun, and Yoshua Bengio · 2022
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Flow annealed importance sampling bootstrap
Laurence Illing Midgley, Vincent Stimper, Gregor NC Simm, Bernhard Schölkopf, and José Miguel Hernández-Lobato · 2022
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SuperNest: accelerated nested sampling applied to astrophysics and cosmology
Aleksandr Petrosyan and William James Handley · 2022
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Exploring phase space with nested sampling
David Yallup, Timo Janßen, Steffen Schumann, and Will Handley · 2022
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Generative flow networks for discrete probabilistic modeling
Dinghuai Zhang, Nikolay Malkin, Zhen Liu, Alexandra Volokhova, Aaron Courville, and Yoshua Bengio · 2022
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Path integral sampler: a stochastic control approach for sampling
Qinsheng Zhang and Yongxin Chen · 2022
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Scalable inference with Autoregressive Neural Ratio Estimation
Noemi Anau Montel, James Alvey, and Christoph Weniger · 2023
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GFlowNet foundations
Yoshua Bengio, Salem Lahlou, Tristan Deleu, Edward J Hu, Mo Tiwari, and Emmanuel Bengio · 2023
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Will Handley · 2023
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A theory of continuous generative flow networks
Salem Lahlou, Tristan Deleu, Pablo Lemos, Dinghuai Zhang, Alexandra Volokhova, Alex Hernández-García, Léna Néhale Ezzine, Yoshua Bengio, and Nikolay Malkin · 2023
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GFlowNets and variational inference
Nikolay Malkin, Salem Lahlou, Tristan Deleu, Xu Ji, Edward Hu, Katie Everett, Dinghuai Zhang, and Yoshua Bengio · 2023
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Proximal nested sampling with data-driven priors for physical scientists
Jason D. McEwen, Tobías I. Liaudat, Matthew A. Price, Xiaohao Cai, and Marcelo Pereyra · 2023
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Importance nested sampling with normalising flows
Michael J. Williams, John Veitch, and Chris Messenger · 2023
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