The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo

18 November 2011

Matthew D. Hoffman

Papers citing "The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo"

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Title
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Marginalized particle Gibbs for multiple state-space models coupled through shared parameters A. Wigren Fredrik Lindsten 13 0 0 13 Oct 2022
Joint control variate for faster black-box variational inference Xi Wang Tomas Geffner Justin Domke BDL DRL 16 0 0 13 Oct 2022
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An Ordinal Latent Variable Model of Conflict Intensity Niklas Stoehr Lucas Torroba Hennigen Josef Valvoda Robert West Ryan Cotterell Aaron Schein 16 8 0 08 Oct 2022
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Physics-Informed Machine Learning of Dynamical Systems for Efficient Bayesian Inference Somayajulu L. N. Dhulipala Yifeng Che Michael D. Shields 33 0 0 19 Sep 2022
Optimal Scaling for Locally Balanced Proposals in Discrete Spaces Haoran Sun H. Dai Dale Schuurmans 22 13 0 16 Sep 2022
On the Dissipation of Ideal Hamiltonian Monte Carlo Sampler Qijia Jiang 30 7 0 15 Sep 2022
Borch: A Deep Universal Probabilistic Programming Language Lewis Belcher Johan Gudmundsson Michael Green BDL AI4CE UQCV 25 0 0 13 Sep 2022
BayesLDM: A Domain-Specific Language for Probabilistic Modeling of Longitudinal Data Ka-Yee Tung Steven De La Torre Mohamed El Mistiri Rebecca Braga De Braganca Eric B. Hekler Misha Pavel D. Rivera Pedja Klasnja D. Spruijt-Metz Benjamin M. Marlin 17 1 0 12 Sep 2022
Investigating the Impact of Model Misspecification in Neural Simulation-based Inference Patrick W Cannon Daniel Ward Sebastian M. Schmon 22 34 0 05 Sep 2022
Bayesian order identification of ARMA models with projection predictive inference Yann McLatchie Asael Alonzo Matamoros David Kohns Aki Vehtari 21 1 0 31 Aug 2022
An approximate diffusion process for environmental stochasticity in infectious disease transmission modelling Sanmitra Ghosh Paul J. Birrell Daniela De Angelis 13 2 0 30 Aug 2022
The case for fully Bayesian optimisation in small-sample trials Yuji Saikai 17 0 0 30 Aug 2022
NeuralUQ: A comprehensive library for uncertainty quantification in neural differential equations and operators Zongren Zou Xuhui Meng Apostolos F. Psaros George Karniadakis AI4CE 25 36 0 25 Aug 2022
Fast emulation of density functional theory simulations using approximate Gaussian processes S. Stetzler M. Grosskopf E. Lawrence 18 0 0 24 Aug 2022
Efficient Utility Function Learning for Multi-Objective Parameter Optimization with Prior Knowledge F. Khan J. Dietrich Christian Wirth 14 1 0 22 Aug 2022
Bias amplification in experimental social networks is reduced by resampling Mathew D. Hardy Bill D. Thompson P. Krafft Thomas L. Griffiths 14 1 0 15 Aug 2022
Intuitive Joint Priors for Bayesian Linear Multilevel Models: The R2D2M2 prior Javier Enrique Aguilar Paul-Christian Burkner 9 25 0 15 Aug 2022
Bayesian Inference with Latent Hamiltonian Neural Networks Somayajulu L. N. Dhulipala Yifeng Che Michael D. Shields BDL 31 3 0 12 Aug 2022
Sampling algorithms in statistical physics: a guide for statistics and machine learning Michael F Faulkner Samuel Livingstone 13 6 0 09 Aug 2022
Computing Bayes: From Then 'Til Now' G. Martin David T. Frazier Christian P. Robert 22 15 0 01 Aug 2022
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The Importance Markov Chain Charly Andral Randal Douc Hugo Marival Christian P. Robert 18 4 0 17 Jul 2022
Split Hamiltonian Monte Carlo revisited F. Casas J. Sanz-Serna Luke Shaw 16 8 0 15 Jul 2022
Lapse risk modelling in insurance: a Bayesian mixture approach Viviana G. R. Lobo T. C. Fonseca M. Alves 11 0 0 14 Jul 2022
BR-SNIS: Bias Reduced Self-Normalized Importance Sampling Gabriel Victorino Cardoso S. Samsonov Achille Thin Eric Moulines Jimmy Olsson 34 6 0 13 Jul 2022
Variational Inference of overparameterized Bayesian Neural Networks: a theoretical and empirical study Tom Huix Szymon Majewski Alain Durmus Eric Moulines Anna Korba BDL 18 6 0 08 Jul 2022
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Reconstructing the Universe with Variational self-Boosted Sampling Chirag Modi Yin Li David M. Blei 13 8 0 28 Jun 2022