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

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

18 November 2011
Matthew D. Hoffman
Andrew Gelman
ArXivPDFHTML

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

50 / 893 papers shown
Title
Efficient MCMC Sampling with Expensive-to-Compute and Irregular Likelihoods
Efficient MCMC Sampling with Expensive-to-Compute and Irregular Likelihoods
Conor Rosato
Harvinder Lehal
Simon Maskell
L. Devlin
Malcolm Strens
10
0
0
15 May 2025
Bayesian Estimation of Causal Effects Using Proxies of a Latent Interference Network
Bayesian Estimation of Causal Effects Using Proxies of a Latent Interference Network
Bar Weinstein
Daniel Nevo
CML
21
0
0
13 May 2025
Uncertainty-Aware Surrogate-based Amortized Bayesian Inference for Computationally Expensive Models
Uncertainty-Aware Surrogate-based Amortized Bayesian Inference for Computationally Expensive Models
Stefania Scheurer
Philipp Reiser
Tim Brünnette
Wolfgang Nowak
A. Guthke
Paul-Christian Burkner
31
0
0
13 May 2025
Improved Uncertainty Quantification in Physics-Informed Neural Networks Using Error Bounds and Solution Bundles
Improved Uncertainty Quantification in Physics-Informed Neural Networks Using Error Bounds and Solution Bundles
P. Flores
Olga Graf
P. Protopapas
K. Pichara
PINN
28
0
0
09 May 2025
Position: Epistemic Artificial Intelligence is Essential for Machine Learning Models to Know When They Do Not Know
Position: Epistemic Artificial Intelligence is Essential for Machine Learning Models to Know When They Do Not Know
Shireen Kudukkil Manchingal
Fabio Cuzzolin
46
0
0
08 May 2025
HiBayES: A Hierarchical Bayesian Modeling Framework for AI Evaluation Statistics
HiBayES: A Hierarchical Bayesian Modeling Framework for AI Evaluation Statistics
Lennart Luettgau
Harry Coppock
Magda Dubois
Christopher Summerfield
Cozmin Ududec
28
0
0
08 May 2025
Boosting Statistic Learning with Synthetic Data from Pretrained Large Models
Boosting Statistic Learning with Synthetic Data from Pretrained Large Models
Jialong Jiang
Wenkang Hu
Jian Huang
Yuling Jiao
Xu Liu
DiffM
45
0
0
08 May 2025
New affine invariant ensemble samplers and their dimensional scaling
New affine invariant ensemble samplers and their dimensional scaling
Yifan Chen
16
0
0
05 May 2025
CoCoAFusE: Beyond Mixtures of Experts via Model Fusion
CoCoAFusE: Beyond Mixtures of Experts via Model Fusion
Aurelio Raffa Ugolini
M. Tanelli
Valentina Breschi
MoE
24
0
0
02 May 2025
Energy-Based Coarse-Graining in Molecular Dynamics: A Flow-Based Framework Without Data
Energy-Based Coarse-Graining in Molecular Dynamics: A Flow-Based Framework Without Data
Maximilian Stupp
P. S. Koutsourelakis
40
0
0
29 Apr 2025
Deep Physics Prior for First Order Inverse Optimization
Deep Physics Prior for First Order Inverse Optimization
Haoyu Yang
Kamyar Azizzadenesheli
Haoxing Ren
PINN
AI4CE
75
0
0
28 Apr 2025
Numerical Generalized Randomized Hamiltonian Monte Carlo for piecewise smooth target densities
Numerical Generalized Randomized Hamiltonian Monte Carlo for piecewise smooth target densities
Jimmy Huy Tran
T. S. Kleppe
BDL
43
0
0
25 Apr 2025
Diffusion-based supervised learning of generative models for efficient sampling of multimodal distributions
Diffusion-based supervised learning of generative models for efficient sampling of multimodal distributions
Hoang Tran
Zezhong Zhang
F. Bao
Dan Lu
Guannan Zhang
DiffM
42
0
0
20 Apr 2025
An Image is Worth $K$ Topics: A Visual Structural Topic Model with Pretrained Image Embeddings
An Image is Worth KKK Topics: A Visual Structural Topic Model with Pretrained Image Embeddings
Matías Piqueras
Alexandra Segerberg
Matteo Magnani
Måns Magnusson
Nataša Sladoje
35
0
0
14 Apr 2025
Improving the evaluation of samplers on multi-modal targets
Improving the evaluation of samplers on multi-modal targets
Louis Grenioux
Maxence Noble
Marylou Gabrié
89
0
0
11 Apr 2025
IAEmu: Learning Galaxy Intrinsic Alignment Correlations
IAEmu: Learning Galaxy Intrinsic Alignment Correlations
Sneh Pandya
Yuanyuan Yang
N. V. Alfen
Jonathan Blazek
Robin Walters
26
1
0
07 Apr 2025
Multi-resolution Score-Based Variational Graphical Diffusion for Causal Disaster System Modeling and Inference
Multi-resolution Score-Based Variational Graphical Diffusion for Causal Disaster System Modeling and Inference
Xuechun Li
Shan Gao
Susu Xu
DiffM
26
0
0
05 Apr 2025
Incorporating the ChEES Criterion into Sequential Monte Carlo Samplers
Incorporating the ChEES Criterion into Sequential Monte Carlo Samplers
Andrew Millard
Joshua Murphy
Daniel Frisch
Simon Maskell
BDL
43
0
0
03 Apr 2025
DeepRV: pre-trained spatial priors for accelerated disease mapping
DeepRV: pre-trained spatial priors for accelerated disease mapping
Jhonathan Navott
Daniel Jenson
Seth Flaxman
Elizaveta Semenova
47
0
0
27 Mar 2025
Efficiently Vectorized MCMC on Modern Accelerators
Efficiently Vectorized MCMC on Modern Accelerators
Hugh Dance
Pierre Glaser
Peter Orbanz
Ryan P. Adams
47
0
0
20 Mar 2025
The Architecture and Evaluation of Bayesian Neural Networks
The Architecture and Evaluation of Bayesian Neural Networks
Alisa Sheinkman
Sara Wade
UQCV
BDL
67
0
0
14 Mar 2025
Learning and planning for optimal synergistic human-robot coordination in manufacturing contexts
Samuele Sandrini
M. Faroni
N. Pedrocchi
58
0
0
10 Mar 2025
Paths and Ambient Spaces in Neural Loss Landscapes
Daniel Dold
Julius Kobialka
Nicolai Palm
Emanuel Sommer
David Rügamer
Oliver Durr
AI4CE
56
0
0
05 Mar 2025
Weighted Euclidean Distance Matrices over Mixed Continuous and Categorical Inputs for Gaussian Process Models
Mingyu Pu
Songhao Wang
Haowei Wang
S. Ng
42
0
0
04 Mar 2025
Differentially private synthesis of Spatial Point Processes
Differentially private synthesis of Spatial Point Processes
Dangchan Kim
Chae Young Lim
60
0
0
25 Feb 2025
In-Context Parametric Inference: Point or Distribution Estimators?
In-Context Parametric Inference: Point or Distribution Estimators?
Sarthak Mittal
Yoshua Bengio
Nikolay Malkin
Guillaume Lajoie
72
0
0
17 Feb 2025
Amortized In-Context Bayesian Posterior Estimation
Sarthak Mittal
Niels Leif Bracher
Guillaume Lajoie
P. Jaini
Marcus A. Brubaker
54
1
0
10 Feb 2025
Microcanonical Langevin Ensembles: Advancing the Sampling of Bayesian Neural Networks
Emanuel Sommer
Jakob Robnik
Giorgi Nozadze
U. Seljak
David Rügamer
BDL
UQCV
84
1
0
10 Feb 2025
Student-t processes as infinite-width limits of posterior Bayesian neural networks
Student-t processes as infinite-width limits of posterior Bayesian neural networks
Francesco Caporali
Stefano Favaro
Dario Trevisan
BDL
150
0
0
06 Feb 2025
Distribution Transformers: Fast Approximate Bayesian Inference With On-The-Fly Prior Adaptation
Distribution Transformers: Fast Approximate Bayesian Inference With On-The-Fly Prior Adaptation
George Whittle
Juliusz Ziomek
Jacob Rawling
Michael A. Osborne
80
2
0
04 Feb 2025
Muti-Fidelity Prediction and Uncertainty Quantification with Laplace Neural Operators for Parametric Partial Differential Equations
Muti-Fidelity Prediction and Uncertainty Quantification with Laplace Neural Operators for Parametric Partial Differential Equations
Haoyang Zheng
Guang Lin
AI4CE
46
0
0
01 Feb 2025
Bayesian Optimization with Preference Exploration by Monotonic Neural Network Ensemble
Bayesian Optimization with Preference Exploration by Monotonic Neural Network Ensemble
Hanyang Wang
Juergen Branke
Matthias Poloczek
97
0
0
30 Jan 2025
CreINNs: Credal-Set Interval Neural Networks for Uncertainty Estimation in Classification Tasks
CreINNs: Credal-Set Interval Neural Networks for Uncertainty Estimation in Classification Tasks
Kaizheng Wang
Keivan K1 Shariatmadar
Shireen Kudukkil Manchingal
Fabio Cuzzolin
David Moens
Hans Hallez
UQCV
BDL
84
12
0
28 Jan 2025
From discrete-time policies to continuous-time diffusion samplers: Asymptotic equivalences and faster training
From discrete-time policies to continuous-time diffusion samplers: Asymptotic equivalences and faster training
Julius Berner
Lorenz Richter
Marcin Sendera
Jarrid Rector-Brooks
Nikolay Malkin
OffRL
58
3
0
10 Jan 2025
Inflationary Flows: Calibrated Bayesian Inference with Diffusion-Based Models
Inflationary Flows: Calibrated Bayesian Inference with Diffusion-Based Models
Daniela de Albuquerque
John Pearson
DiffM
51
0
0
03 Jan 2025
Improving Pareto Set Learning for Expensive Multi-objective Optimization via Stein Variational Hypernetworks
Improving Pareto Set Learning for Expensive Multi-objective Optimization via Stein Variational Hypernetworks
Minh-Duc Nguyen
Phuong Mai Dinh
Quang-Huy Nguyen
L. P. Hoang
Dung D. Le
43
1
0
23 Dec 2024
Empirical evaluation of normalizing flows in Markov Chain Monte Carlo
Empirical evaluation of normalizing flows in Markov Chain Monte Carlo
David Nabergoj
Erik Štrumbelj
BDL
TPM
38
0
0
22 Dec 2024
Exponential speed up in Monte Carlo sampling through Radial Updates
Exponential speed up in Monte Carlo sampling through Radial Updates
Johann Ostmeyer
66
2
0
27 Nov 2024
Cautious Optimizers: Improving Training with One Line of Code
Cautious Optimizers: Improving Training with One Line of Code
Kaizhao Liang
Lizhang Chen
B. Liu
Qiang Liu
ODL
103
5
0
25 Nov 2024
Predicting Emergent Capabilities by Finetuning
Predicting Emergent Capabilities by Finetuning
Charlie Snell
Eric Wallace
Dan Klein
Sergey Levine
ELM
LRM
75
5
0
25 Nov 2024
Benchmarking Active Learning for NILM
Benchmarking Active Learning for NILM
Dhruv Patel
Ankita Kumari Jain
Haikoo Khandor
Xhitij Choudhary
Nipun Batra
66
0
0
24 Nov 2024
Bayesian Calibration of Win Rate Estimation with LLM Evaluators
Bayesian Calibration of Win Rate Estimation with LLM Evaluators
Yicheng Gao
G. Xu
Zhe Wang
Arman Cohan
36
6
0
07 Nov 2024
Running Markov Chain Monte Carlo on Modern Hardware and Software
Running Markov Chain Monte Carlo on Modern Hardware and Software
Pavel Sountsov
Colin Carroll
Matthew D. Hoffman
BDL
34
2
0
06 Nov 2024
On MCMC mixing under unidentified nonparametric models with an
  application to survival predictions under transformation models
On MCMC mixing under unidentified nonparametric models with an application to survival predictions under transformation models
Chong Zhong
Jin Yang
Junshan Shen
Catherine C. Liu
Zhaohai Li
29
0
0
03 Nov 2024
Hamiltonian Monte Carlo Inference of Marginalized Linear Mixed-Effects Models
Hamiltonian Monte Carlo Inference of Marginalized Linear Mixed-Effects Models
Jinlin Lai
Justin Domke
Daniel Sheldon
26
0
0
31 Oct 2024
Flow Matching for Posterior Inference with Simulator Feedback
Flow Matching for Posterior Inference with Simulator Feedback
Benjamin Holzschuh
Nils Thuerey
25
0
0
29 Oct 2024
Bayesian shared parameter joint models for heterogeneous populations
Bayesian shared parameter joint models for heterogeneous populations
Sida Chen
Danilo Alvares
Marco Palma
Jessica K. Barrett
21
0
0
29 Oct 2024
ATLAS: Adapting Trajectory Lengths and Step-Size for Hamiltonian Monte
  Carlo
ATLAS: Adapting Trajectory Lengths and Step-Size for Hamiltonian Monte Carlo
Chirag Modi
23
1
0
28 Oct 2024
Hamiltonian Score Matching and Generative Flows
Hamiltonian Score Matching and Generative Flows
Peter Holderrieth
Yilun Xu
Tommi Jaakkola
26
0
0
27 Oct 2024
Low-rank Bayesian matrix completion via geodesic Hamiltonian Monte Carlo
  on Stiefel manifolds
Low-rank Bayesian matrix completion via geodesic Hamiltonian Monte Carlo on Stiefel manifolds
Tiangang Cui
Alex Gorodetsky
13
0
0
27 Oct 2024
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