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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
Flexible Heteroscedastic Count Regression with Deep Double Poisson
  Networks
Flexible Heteroscedastic Count Regression with Deep Double Poisson Networks
Spencer Young
P. Jenkins
Lonchao Da
Jeff Dotson
Hua Wei
UQCV
BDL
31
2
0
13 Jun 2024
Assessment of Uncertainty Quantification in Universal Differential
  Equations
Assessment of Uncertainty Quantification in Universal Differential Equations
Nina Schmid
David Fernandes del Pozo
Willem Waegeman
Jan Hasenauer
AI4CE
53
1
0
13 Jun 2024
Personalized Product Assortment with Real-time 3D Perception and
  Bayesian Payoff Estimation
Personalized Product Assortment with Real-time 3D Perception and Bayesian Payoff Estimation
P. Jenkins
Michael Selander
J. S. Jenkins
Andrew Merrill
Kyle Armstrong
23
1
0
11 Jun 2024
Sampling From Multiscale Densities With Delayed Rejection Generalized
  Hamiltonian Monte Carlo
Sampling From Multiscale Densities With Delayed Rejection Generalized Hamiltonian Monte Carlo
Gilad Turok
Chirag Modi
Bob Carpenter
BDL
32
3
0
04 Jun 2024
Fearless Stochasticity in Expectation Propagation
Fearless Stochasticity in Expectation Propagation
Jonathan So
Richard E. Turner
37
0
0
03 Jun 2024
The Surprising Effectiveness of SP Voting with Partial Preferences
The Surprising Effectiveness of SP Voting with Partial Preferences
Hadi Hosseini
Debmalya Mandal
Amrit Puhan
39
2
0
02 Jun 2024
Scalable Bayesian Learning with posteriors
Scalable Bayesian Learning with posteriors
Samuel Duffield
Kaelan Donatella
Johnathan Chiu
Phoebe Klett
Daniel Simpson
BDL
UQCV
62
1
0
31 May 2024
Crowdsourcing with Difficulty: A Bayesian Rating Model for Heterogeneous
  Items
Crowdsourcing with Difficulty: A Bayesian Rating Model for Heterogeneous Items
Seong Woo Han
Ozan Adigüzel
Bob Carpenter
49
0
0
29 May 2024
Federated Learning for Non-factorizable Models using Deep Generative
  Prior Approximations
Federated Learning for Non-factorizable Models using Deep Generative Prior Approximations
Conor Hassan
Joshua J Bon
Elizaveta Semenova
Antonietta Mira
Kerrie Mengersen
26
0
0
25 May 2024
Prediction of cancer dynamics under treatment using Bayesian neural
  networks: A simulated study
Prediction of cancer dynamics under treatment using Bayesian neural networks: A simulated study
E. M. Myklebust
A. Frigessi
Fredrik Schjesvold
J. Foo
K. Leder
Alvaro Kohn-Luque
AI4CE
27
0
0
23 May 2024
A Direct Importance Sampling-based Framework for Rare Event Uncertainty
  Quantification in Non-Gaussian Spaces
A Direct Importance Sampling-based Framework for Rare Event Uncertainty Quantification in Non-Gaussian Spaces
Elsayed M. Eshra
Konstantinos G. Papakonstantinou
Hamed Nikbakht
23
0
0
23 May 2024
Credal Wrapper of Model Averaging for Uncertainty Estimation in Classification
Credal Wrapper of Model Averaging for Uncertainty Estimation in Classification
Kaizheng Wang
Fabio Cuzzolin
Keivan K1 Shariatmadar
David Moens
Hans Hallez
UQCV
BDL
77
6
0
23 May 2024
Reinforcement Learning for Adaptive MCMC
Reinforcement Learning for Adaptive MCMC
Congye Wang
Wilson Chen
Heishiro Kanagawa
Chris J. Oates
BDL
21
2
0
22 May 2024
Active Learning with Fully Bayesian Neural Networks for Discontinuous
  and Nonstationary Data
Active Learning with Fully Bayesian Neural Networks for Discontinuous and Nonstationary Data
Maxim Ziatdinov
AI4CE
19
4
0
16 May 2024
Multi-fidelity Hamiltonian Monte Carlo
Multi-fidelity Hamiltonian Monte Carlo
Dhruv V. Patel
Jonghyun Lee
Matthew W. Farthing
P. Kitanidis
Eric F. Darve
42
0
0
08 May 2024
S$^2$AC: Energy-Based Reinforcement Learning with Stein Soft Actor
  Critic
S2^22AC: Energy-Based Reinforcement Learning with Stein Soft Actor Critic
Safa Messaoud
Billel Mokeddem
Zhenghai Xue
L. Pang
Bo An
Haipeng Chen
Sanjay Chawla
41
3
0
02 May 2024
GIST: Gibbs self-tuning for locally adaptive Hamiltonian Monte Carlo
GIST: Gibbs self-tuning for locally adaptive Hamiltonian Monte Carlo
Nawaf Bou-Rabee
Bob Carpenter
Milo Marsden
43
6
0
23 Apr 2024
Why not a thin plate spline for spatial models? A comparative study
  using Bayesian inference
Why not a thin plate spline for spatial models? A comparative study using Bayesian inference
J. Cavieres
Paula Moraga
Cole C. Monnahan
16
0
0
19 Apr 2024
Variational Bayesian Optimal Experimental Design with Normalizing Flows
Variational Bayesian Optimal Experimental Design with Normalizing Flows
Jiayuan Dong
Christian L. Jacobsen
Mehdi Khalloufi
Maryam Akram
Wanjiao Liu
Karthik Duraisamy
Xun Huan
BDL
54
5
0
08 Apr 2024
Supporting Bayesian modelling workflows with iterative filtering for
  multiverse analysis
Supporting Bayesian modelling workflows with iterative filtering for multiverse analysis
Anna Elisabeth Riha
Nikolas Siccha
Antti Oulasvirta
Aki Vehtari
25
0
0
02 Apr 2024
Incentives in Private Collaborative Machine Learning
Incentives in Private Collaborative Machine Learning
Rachael Hwee Ling Sim
Yehong Zhang
Nghia Hoang
Xinyi Xu
K. H. Low
P. Jaillet
25
4
0
02 Apr 2024
Divide, Conquer, Combine Bayesian Decision Tree Sampling
Divide, Conquer, Combine Bayesian Decision Tree Sampling
Jodie A. Cochrane
Adrian G. Wills
Sarah J. Johnson
24
1
0
26 Mar 2024
Towards Multilevel Modelling of Train Passing Events on the
  Staffordshire Bridge
Towards Multilevel Modelling of Train Passing Events on the Staffordshire Bridge
L. Bull
Chiho Jeon
M. Girolami
Andrew Duncan
Jennifer Schooling
Miguel Bravo Haro
12
0
0
26 Mar 2024
Bayesian inversion with Student's t priors based on Gaussian scale
  mixtures
Bayesian inversion with Student's t priors based on Gaussian scale mixtures
A. Senchukova
Felipe Uribe
L. Roininen
19
3
0
20 Mar 2024
Variational Inference for Uncertainty Quantification: an Analysis of Trade-offs
Variational Inference for Uncertainty Quantification: an Analysis of Trade-offs
C. Margossian
Loucas Pillaud-Vivien
Lawrence K. Saul
UD
71
2
0
20 Mar 2024
VISA: Variational Inference with Sequential Sample-Average
  Approximations
VISA: Variational Inference with Sequential Sample-Average Approximations
Heiko Zimmermann
C. A. Naesseth
Jan Willem van de Meent
25
0
0
14 Mar 2024
Scalability of Metropolis-within-Gibbs schemes for high-dimensional
  Bayesian models
Scalability of Metropolis-within-Gibbs schemes for high-dimensional Bayesian models
Filippo Ascolani
Gareth O. Roberts
Giacomo Zanella
42
6
0
14 Mar 2024
Tuning diagonal scale matrices for HMC
Tuning diagonal scale matrices for HMC
Jimmy Huy Tran
T. S. Kleppe
17
2
0
12 Mar 2024
Bayesian Hierarchical Probabilistic Forecasting of Intraday Electricity
  Prices
Bayesian Hierarchical Probabilistic Forecasting of Intraday Electricity Prices
Daniel Nickelsen
Gernot Müller
AI4TS
16
2
0
08 Mar 2024
Variational Inference of Parameters in Opinion Dynamics Models
Variational Inference of Parameters in Opinion Dynamics Models
Jacopo Lenti
Fabrizio Silvestri
G. D. F. Morales
29
3
0
08 Mar 2024
Repelling-Attracting Hamiltonian Monte Carlo
Repelling-Attracting Hamiltonian Monte Carlo
Siddharth Vishwanath
Hyungsuk Tak
16
0
0
07 Mar 2024
Offensive Lineup Analysis in Basketball with Clustering Players Based on
  Shooting Style and Offensive Role
Offensive Lineup Analysis in Basketball with Clustering Players Based on Shooting Style and Offensive Role
Kazuhiro Yamada
Keisuke Fujii
14
0
0
04 Mar 2024
Anomaly Detection in Offshore Wind Turbine Structures using Hierarchical
  Bayesian Modelling
Anomaly Detection in Offshore Wind Turbine Structures using Hierarchical Bayesian Modelling
Simon M. Smith
Aidan J. Hughe
T. Dardeno
L. Bull
N. Dervilis
K. Worden
11
1
0
29 Feb 2024
Copula Approximate Bayesian Computation Using Distribution Random
  Forests
Copula Approximate Bayesian Computation Using Distribution Random Forests
G. Karabatsos
37
1
0
28 Feb 2024
Automated Statistical Model Discovery with Language Models
Automated Statistical Model Discovery with Language Models
Michael Y. Li
Emily B. Fox
Noah D. Goodman
34
14
0
27 Feb 2024
Stable Training of Normalizing Flows for High-dimensional Variational
  Inference
Stable Training of Normalizing Flows for High-dimensional Variational Inference
Daniel Andrade
BDL
TPM
43
1
0
26 Feb 2024
Accelerating Convergence of Stein Variational Gradient Descent via Deep
  Unfolding
Accelerating Convergence of Stein Variational Gradient Descent via Deep Unfolding
Yuya Kawamura
Satoshi Takabe
BDL
24
0
0
23 Feb 2024
Stacking Factorizing Partitioned Expressions in Hybrid Bayesian Network
  Models
Stacking Factorizing Partitioned Expressions in Hybrid Bayesian Network Models
Peng Lin
M. Neil
Norman E. Fenton
16
0
0
23 Feb 2024
Dissecting Human and LLM Preferences
Dissecting Human and LLM Preferences
Junlong Li
Fan Zhou
Shichao Sun
Yikai Zhang
Hai Zhao
Pengfei Liu
ALM
21
5
0
17 Feb 2024
BlackJAX: Composable Bayesian inference in JAX
BlackJAX: Composable Bayesian inference in JAX
Alberto Cabezas
Adrien Corenflos
Junpeng Lao
Rémi Louf
Antoine Carnec
...
Kevin P. Murphy
Juan Camilo Orduz
Karm Patel
Xi Wang
Robert Zinkov
DRL
MLAU
33
18
0
16 Feb 2024
Examining Pathological Bias in a Generative Adversarial Network
  Discriminator: A Case Study on a StyleGAN3 Model
Examining Pathological Bias in a Generative Adversarial Network Discriminator: A Case Study on a StyleGAN3 Model
Alvin Grissom II
Ryan F. Lei
Matt Gusdorff
Jeova Farias Sales Rocha Neto
Bailey Lin
Ryan Trotter
GAN
22
0
0
15 Feb 2024
Parameterizations for Gradient-based Markov Chain Monte Carlo on the
  Stiefel Manifold: A Comparative Study
Parameterizations for Gradient-based Markov Chain Monte Carlo on the Stiefel Manifold: A Comparative Study
Masahiro Tanaka
22
1
0
12 Feb 2024
Iterated Denoising Energy Matching for Sampling from Boltzmann Densities
Iterated Denoising Energy Matching for Sampling from Boltzmann Densities
Tara Akhound-Sadegh
Jarrid Rector-Brooks
A. Bose
Sarthak Mittal
Pablo Lemos
...
Siamak Ravanbakhsh
Gauthier Gidel
Yoshua Bengio
Nikolay Malkin
Alexander Tong
DiffM
34
41
0
09 Feb 2024
Improved off-policy training of diffusion samplers
Improved off-policy training of diffusion samplers
Marcin Sendera
Minsu Kim
Sarthak Mittal
Pablo Lemos
Luca Scimeca
Jarrid Rector-Brooks
Alexandre Adam
Yoshua Bengio
Nikolay Malkin
OffRL
66
17
0
07 Feb 2024
PQMass: Probabilistic Assessment of the Quality of Generative Models using Probability Mass Estimation
PQMass: Probabilistic Assessment of the Quality of Generative Models using Probability Mass Estimation
Pablo Lemos
Sammy N. Sharief
Nikolay Malkin
Laurence Perreault Levasseur
Y. Hezaveh
Laurence Perreault-Levasseur
Yashar Hezaveh
19
3
0
06 Feb 2024
Enhancing Score-Based Sampling Methods with Ensembles
Enhancing Score-Based Sampling Methods with Ensembles
T. Bischoff
B. Riel
15
1
0
31 Jan 2024
Recovering Mental Representations from Large Language Models with Markov
  Chain Monte Carlo
Recovering Mental Representations from Large Language Models with Markov Chain Monte Carlo
Jian-Qiao Zhu
Haijiang Yan
Thomas L. Griffiths
25
2
0
30 Jan 2024
Parallel Affine Transformation Tuning of Markov Chain Monte Carlo
Parallel Affine Transformation Tuning of Markov Chain Monte Carlo
Philip Schär
Michael Habeck
Daniel Rudolf
16
1
0
29 Jan 2024
Accelerating Approximate Thompson Sampling with Underdamped Langevin
  Monte Carlo
Accelerating Approximate Thompson Sampling with Underdamped Langevin Monte Carlo
Haoyang Zheng
Wei Deng
Christian Moya
Guang Lin
22
6
0
22 Jan 2024
Faster ISNet for Background Bias Mitigation on Deep Neural Networks
Faster ISNet for Background Bias Mitigation on Deep Neural Networks
P. R. Bassi
S. Decherchi
Andrea Cavalli
17
0
0
16 Jan 2024
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