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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 / 894 papers shown
Title
Automatic Zig-Zag sampling in practice
Automatic Zig-Zag sampling in practice
Alice Corbella
S. Spencer
Gareth O. Roberts
10
20
0
22 Jun 2022
Learning Physics between Digital Twins with Low-Fidelity Models and
  Physics-Informed Gaussian Processes
Learning Physics between Digital Twins with Low-Fidelity Models and Physics-Informed Gaussian Processes
Michail Spitieris
I. Steinsland
AI4CE
13
0
0
16 Jun 2022
Bayesian conjugacy in probit, tobit, multinomial probit and extensions:
  A review and new results
Bayesian conjugacy in probit, tobit, multinomial probit and extensions: A review and new results
Niccolò Anceschi
A. Fasano
Daniele Durante
Giacomo Zanella
23
16
0
16 Jun 2022
Bayesian NVH metamodels to assess interior cabin noise using measurement
  databases
Bayesian NVH metamodels to assess interior cabin noise using measurement databases
V. Prakash
O. Sauvage
J. Antoni
L. Gagliardini
17
1
0
12 Jun 2022
A Probabilistic Machine Learning Approach to Scheduling Parallel Loops
  with Bayesian Optimization
A Probabilistic Machine Learning Approach to Scheduling Parallel Loops with Bayesian Optimization
Kyurae Kim
Youngjae Kim
Sungyong Park
21
12
0
12 Jun 2022
Density Regression and Uncertainty Quantification with Bayesian Deep
  Noise Neural Networks
Density Regression and Uncertainty Quantification with Bayesian Deep Noise Neural Networks
Daiwei Zhang
Tianci Liu
Jian Kang
BDL
UQCV
35
2
0
12 Jun 2022
Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel
  Recombination
Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel Recombination
Masaki Adachi
Satoshi Hayakawa
Martin Jørgensen
Harald Oberhauser
Michael A. Osborne
14
19
0
09 Jun 2022
Randomized Time Riemannian Manifold Hamiltonian Monte Carlo
Randomized Time Riemannian Manifold Hamiltonian Monte Carlo
P. Whalley
Daniel Paulin
B. Leimkuhler
8
4
0
09 Jun 2022
Crosslinguistic word order variation reflects evolutionary pressures of
  dependency and information locality
Crosslinguistic word order variation reflects evolutionary pressures of dependency and information locality
Michael Hahn
Yang Xu
6
16
0
09 Jun 2022
Bayesian additive regression trees for probabilistic programming
Bayesian additive regression trees for probabilistic programming
Miriana Quiroga
P. Garay
J. M. Alonso
J. M. Loyola
Osvaldo A. Martin
21
5
0
08 Jun 2022
Mesh-free Eulerian Physics-Informed Neural Networks
Mesh-free Eulerian Physics-Informed Neural Networks
F. A. Torres
M. Negri
Monika Nagy-Huber
M. Samarin
Volker Roth
PINN
AI4CE
13
5
0
03 Jun 2022
Masked Bayesian Neural Networks : Computation and Optimality
Insung Kong
Dongyoon Yang
Jongjin Lee
Ilsang Ohn
Yongdai Kim
TPM
22
1
0
02 Jun 2022
Core-periphery Models for Hypergraphs
Core-periphery Models for Hypergraphs
Marios Papachristou
Jon M. Kleinberg
LRM
10
5
0
01 Jun 2022
Asymptotic Properties for Bayesian Neural Network in Besov Space
Asymptotic Properties for Bayesian Neural Network in Besov Space
Kyeongwon Lee
Jaeyong Lee
BDL
11
4
0
01 Jun 2022
Easy Variational Inference for Categorical Models via an Independent
  Binary Approximation
Easy Variational Inference for Categorical Models via an Independent Binary Approximation
M. Wojnowicz
Shuchin Aeron
Eric L. Miller
M. C. Hughes
13
2
0
31 May 2022
Comparing interpretation methods in mental state decoding analyses with
  deep learning models
Comparing interpretation methods in mental state decoding analyses with deep learning models
A. Thomas
Christopher Ré
R. Poldrack
AI4CE
8
2
0
31 May 2022
Noise-Aware Statistical Inference with Differentially Private Synthetic
  Data
Noise-Aware Statistical Inference with Differentially Private Synthetic Data
Ossi Raisa
Joonas Jälkö
Samuel Kaski
Antti Honkela
SyDa
37
10
0
28 May 2022
Deterministic Langevin Monte Carlo with Normalizing Flows for Bayesian
  Inference
Deterministic Langevin Monte Carlo with Normalizing Flows for Bayesian Inference
R. Grumitt
B. Dai
U. Seljak
BDL
26
12
0
27 May 2022
A Quadrature Rule combining Control Variates and Adaptive Importance
  Sampling
A Quadrature Rule combining Control Variates and Adaptive Importance Sampling
Rémi Leluc
Franccois Portier
Johan Segers
Aigerim Zhuman
14
2
0
24 May 2022
Bayesian Active Learning with Fully Bayesian Gaussian Processes
Bayesian Active Learning with Fully Bayesian Gaussian Processes
Christoffer Riis
Francisco Antunes
F. B. Hüttel
C. L. Azevedo
Francisco Câmara Pereira
GP
11
22
0
20 May 2022
Posterior Refinement Improves Sample Efficiency in Bayesian Neural
  Networks
Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks
Agustinus Kristiadi
Runa Eschenhagen
Philipp Hennig
BDL
21
12
0
20 May 2022
Bayesian Discrete Conditional Transformation Models
Bayesian Discrete Conditional Transformation Models
Manuel Carlan
Thomas Kneib
16
2
0
17 May 2022
Fat-Tailed Variational Inference with Anisotropic Tail Adaptive Flows
Fat-Tailed Variational Inference with Anisotropic Tail Adaptive Flows
Feynman T. Liang
Liam Hodgkinson
Michael W. Mahoney
8
11
0
16 May 2022
The AI Teacher Test: Measuring the Pedagogical Ability of Blender and
  GPT-3 in Educational Dialogues
The AI Teacher Test: Measuring the Pedagogical Ability of Blender and GPT-3 in Educational Dialogues
Anaïs Tack
Chris Piech
ELM
32
90
0
16 May 2022
MixFlows: principled variational inference via mixed flows
MixFlows: principled variational inference via mixed flows
Zuheng Xu
Na Chen
Trevor Campbell
55
8
0
16 May 2022
On the use of a local $\hat{R}$ to improve MCMC convergence diagnostic
On the use of a local R^\hat{R}R^ to improve MCMC convergence diagnostic
Théo Moins
Julyan Arbel
A. Dutfoy
Stéphane Girard
22
12
0
13 May 2022
Bayesian Physics-Informed Neural Networks for real-world nonlinear
  dynamical systems
Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems
K. Linka
Amelie Schäfer
Xuhui Meng
Zongren Zou
George Karniadakis
E. Kuhl
OOD
PINN
AI4CE
37
109
0
12 May 2022
Modeling and mitigation of occupational safety risks in dynamic
  industrial environments
Modeling and mitigation of occupational safety risks in dynamic industrial environments
Ashutosh Tewari
António R. C. Paiva
14
1
0
02 May 2022
Meta-free few-shot learning via representation learning with weight
  averaging
Meta-free few-shot learning via representation learning with weight averaging
Kuilin Chen
Chi-Guhn Lee
19
5
0
26 Apr 2022
Hierarchical Bayesian Modelling for Knowledge Transfer Across
  Engineering Fleets via Multitask Learning
Hierarchical Bayesian Modelling for Knowledge Transfer Across Engineering Fleets via Multitask Learning
L. Bull
D. D. Francesco
M. Dhada
O. Steinert
Tony Lindgren
A. Parlikad
A. B. Duncan
M. Girolami
17
27
0
26 Apr 2022
Investigating the efficiency of marginalising over discrete parameters
  in Bayesian computations
Investigating the efficiency of marginalising over discrete parameters in Bayesian computations
Wen Zhang
Jeffrey M. Pullin
L. Gurrin
Damjan Vukcevic
25
1
0
13 Apr 2022
A fully Bayesian sparse polynomial chaos expansion approach with joint
  priors on the coefficients and global selection of terms
A fully Bayesian sparse polynomial chaos expansion approach with joint priors on the coefficients and global selection of terms
Paul-Christian Bürkner
Ilja Kroker
S. Oladyshkin
Wolfgang Nowak
16
10
0
12 Apr 2022
Does the Market of Citations Reward Reproducible Work?
Does the Market of Citations Reward Reproducible Work?
Edward Raff
HAI
CML
12
12
0
08 Apr 2022
Statistical Model Criticism of Variational Auto-Encoders
Statistical Model Criticism of Variational Auto-Encoders
Claartje Barkhof
Wilker Aziz
DRL
19
3
0
06 Apr 2022
MCMC for GLMMs
MCMC for GLMMs
Vivekananda Roy
14
2
0
04 Apr 2022
Direct Sampling with a Step Function
Direct Sampling with a Step Function
Andrew M. Raim
14
1
0
29 Mar 2022
Geometric Methods for Sampling, Optimisation, Inference and Adaptive
  Agents
Geometric Methods for Sampling, Optimisation, Inference and Adaptive Agents
Alessandro Barp
Lancelot Da Costa
G. Francca
Karl J. Friston
Mark Girolami
Michael I. Jordan
G. Pavliotis
31
25
0
20 Mar 2022
Sampling Bias Correction for Supervised Machine Learning: A Bayesian
  Inference Approach with Practical Applications
Sampling Bias Correction for Supervised Machine Learning: A Bayesian Inference Approach with Practical Applications
Max Sklar
FaML
14
1
0
11 Mar 2022
Econometric Modeling of Intraday Electricity Market Price with
  Inadequate Historical Data
Econometric Modeling of Intraday Electricity Market Price with Inadequate Historical Data
Saeed Mohammadi
M. Hesamzadeh
8
4
0
11 Mar 2022
Bayesian inference via sparse Hamiltonian flows
Bayesian inference via sparse Hamiltonian flows
Na Chen
Zuheng Xu
Trevor Campbell
35
14
0
11 Mar 2022
Discovering Inductive Bias with Gibbs Priors: A Diagnostic Tool for
  Approximate Bayesian Inference
Discovering Inductive Bias with Gibbs Priors: A Diagnostic Tool for Approximate Bayesian Inference
Luca Rendsburg
Agustinus Kristiadi
Philipp Hennig
U. V. Luxburg
16
2
0
07 Mar 2022
py-irt: A Scalable Item Response Theory Library for Python
py-irt: A Scalable Item Response Theory Library for Python
John P. Lalor
Pedro Rodriguez
25
10
0
02 Mar 2022
Markov Chain Monte Carlo-Based Machine Unlearning: Unlearning What Needs
  to be Forgotten
Markov Chain Monte Carlo-Based Machine Unlearning: Unlearning What Needs to be Forgotten
Q. Nguyen
Ryutaro Oikawa
D. Divakaran
M. Chan
K. H. Low
MU
17
37
0
28 Feb 2022
Metropolis Adjusted Langevin Trajectories: a robust alternative to
  Hamiltonian Monte Carlo
Metropolis Adjusted Langevin Trajectories: a robust alternative to Hamiltonian Monte Carlo
L. Riou-Durand
Jure Vogrinc
18
14
0
26 Feb 2022
Parallel MCMC Without Embarrassing Failures
Parallel MCMC Without Embarrassing Failures
Daniel Augusto R. M. A. de Souza
Diego Mesquita
Samuel Kaski
Luigi Acerbi
36
11
0
22 Feb 2022
UncertaINR: Uncertainty Quantification of End-to-End Implicit Neural
  Representations for Computed Tomography
UncertaINR: Uncertainty Quantification of End-to-End Implicit Neural Representations for Computed Tomography
Francisca Vasconcelos
Bobby He
Nalini Singh
Yee Whye Teh
BDL
OOD
UQCV
26
12
0
22 Feb 2022
Bayesian Optimisation for Active Monitoring of Air Pollution
Bayesian Optimisation for Active Monitoring of Air Pollution
Sigrid Passano Hellan
Christopher G. Lucas
Nigel H. Goddard
13
10
0
15 Feb 2022
Nonlinear MCMC for Bayesian Machine Learning
Nonlinear MCMC for Bayesian Machine Learning
James Vuckovic
30
2
0
11 Feb 2022
Multilevel Delayed Acceptance MCMC
Multilevel Delayed Acceptance MCMC
Mikkel B. Lykkegaard
T. Dodwell
C. Fox
Grigorios Mingas
Robert Scheichl
22
14
0
08 Feb 2022
Fourier Representations for Black-Box Optimization over Categorical
  Variables
Fourier Representations for Black-Box Optimization over Categorical Variables
Hamid Dadkhahi
Jesus Rios
Karthikeyan Shanmugam
Payel Das
27
11
0
08 Feb 2022
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