ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1111.4246
  4. Cited By
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
Energy-guided Entropic Neural Optimal Transport
Energy-guided Entropic Neural Optimal Transport
Petr Mokrov
Alexander Korotin
Alexander Kolesov
Nikita Gushchin
Evgeny Burnaev
OT
49
21
0
12 Apr 2023
Black Box Variational Inference with a Deterministic Objective: Faster,
  More Accurate, and Even More Black Box
Black Box Variational Inference with a Deterministic Objective: Faster, More Accurate, and Even More Black Box
Ryan Giordano
Martin Ingram
Tamara Broderick
58
12
0
11 Apr 2023
PriorCVAE: scalable MCMC parameter inference with Bayesian deep
  generative modelling
PriorCVAE: scalable MCMC parameter inference with Bayesian deep generative modelling
Elizaveta Semenova
Prakhar Verma
Max Cairney-Leeming
Arno Solin
Samir Bhatt
Seth Flaxman
BDL
22
3
0
09 Apr 2023
Towards Efficient MCMC Sampling in Bayesian Neural Networks by
  Exploiting Symmetry
Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry
J. G. Wiese
Lisa Wimmer
Theodore Papamarkou
Bernd Bischl
Stephan Günnemann
David Rügamer
24
11
0
06 Apr 2023
Theoretical guarantees for neural control variates in MCMC
Theoretical guarantees for neural control variates in MCMC
Denis Belomestny
Artur Goldman
A. Naumov
S. Samsonov
BDL
DRL
11
6
0
03 Apr 2023
Bayesian neural networks via MCMC: a Python-based tutorial
Bayesian neural networks via MCMC: a Python-based tutorial
Rohitash Chandra
Royce Chen
Joshua Simmons
BDL
31
10
0
02 Apr 2023
A Practitioner's Guide to Bayesian Inference in Pharmacometrics using
  Pumas
A Practitioner's Guide to Bayesian Inference in Pharmacometrics using Pumas
Mohamed Tarek
J. Storópoli
Casey B. Davis
C. Elrod
Julius Krumbiegel
Chris Rackauckas
V. Ivaturi
GP
15
3
0
31 Mar 2023
Fluctuation without dissipation: Microcanonical Langevin Monte Carlo
Fluctuation without dissipation: Microcanonical Langevin Monte Carlo
Jakob Robnik
U. Seljak
43
6
0
31 Mar 2023
Generalised Linear Mixed Model Specification, Analysis, Fitting, and
  Optimal Design in R with the glmmr Packages
Generalised Linear Mixed Model Specification, Analysis, Fitting, and Optimal Design in R with the glmmr Packages
S. Watson
18
3
0
22 Mar 2023
Bayesian Pseudo-Coresets via Contrastive Divergence
Bayesian Pseudo-Coresets via Contrastive Divergence
Piyush Tiwary
Kumar Shubham
V. Kashyap
Prathosh A.P.
21
3
0
20 Mar 2023
Neural Probabilistic Logic Programming in Discrete-Continuous Domains
Neural Probabilistic Logic Programming in Discrete-Continuous Domains
Lennert De Smet
Pedro Zuidberg Dos Martires
Robin Manhaeve
G. Marra
Angelika Kimmig
Luc de Raedt
NAI
11
20
0
08 Mar 2023
Eryn : A multi-purpose sampler for Bayesian inference
Eryn : A multi-purpose sampler for Bayesian inference
N. Karnesis
Michael L. Katz
N. Korsakova
J. Gair
N. Stergioulas
13
28
0
03 Mar 2023
Bayesian Quantification with Black-Box Estimators
Bayesian Quantification with Black-Box Estimators
Albert Ziegler
Paweł Czyż
UQCV
18
0
0
17 Feb 2023
Piecewise Deterministic Markov Processes for Bayesian Neural Networks
Piecewise Deterministic Markov Processes for Bayesian Neural Networks
Ethan Goan
Dimitri Perrin
Kerrie Mengersen
Clinton Fookes
20
0
0
17 Feb 2023
Thermodynamic AI and the fluctuation frontier
Thermodynamic AI and the fluctuation frontier
Patrick J. Coles
Collin Szczepanski
Denis Melanson
Kaelan Donatella
Antonio J. Martinez
Faris M. Sbahi
AI4CE
34
16
0
09 Feb 2023
Gibbsian polar slice sampling
Gibbsian polar slice sampling
Philip Schär
Michael Habeck
Daniel Rudolf
11
9
0
08 Feb 2023
Federated Variational Inference Methods for Structured Latent Variable
  Models
Federated Variational Inference Methods for Structured Latent Variable Models
Conor Hassan
Roberto Salomone
Kerrie Mengersen
BDL
FedML
16
4
0
07 Feb 2023
Fixed-kinetic Neural Hamiltonian Flows for enhanced interpretability and
  reduced complexity
Fixed-kinetic Neural Hamiltonian Flows for enhanced interpretability and reduced complexity
Vincent Souveton
Arnaud Guillin
J. Jasche
G. Lavaux
Manon Michel
18
3
0
03 Feb 2023
Automatically Marginalized MCMC in Probabilistic Programming
Automatically Marginalized MCMC in Probabilistic Programming
Jinlin Lai
Javier Burroni
Hui Guan
Daniel Sheldon
18
3
0
01 Feb 2023
Improving and generalizing flow-based generative models with minibatch
  optimal transport
Improving and generalizing flow-based generative models with minibatch optimal transport
Alexander Tong
Kilian Fatras
Nikolay Malkin
G. Huguet
Yanlei Zhang
Jarrid Rector-Brooks
Guy Wolf
Yoshua Bengio
OOD
DiffM
OT
35
230
0
01 Feb 2023
Misspecification-robust Sequential Neural Likelihood for
  Simulation-based Inference
Misspecification-robust Sequential Neural Likelihood for Simulation-based Inference
Ryan P. Kelly
David J. Nott
David T. Frazier
D. Warne
Christopher C. Drovandi
25
10
0
31 Jan 2023
A theory of continuous generative flow networks
A theory of continuous generative flow networks
Salem Lahlou
T. Deleu
Pablo Lemos
Dinghuai Zhang
Alexandra Volokhova
Alex Hernández-García
Léna Néhale Ezzine
Yoshua Bengio
Nikolay Malkin
AI4CE
39
79
0
30 Jan 2023
AutoPEFT: Automatic Configuration Search for Parameter-Efficient
  Fine-Tuning
AutoPEFT: Automatic Configuration Search for Parameter-Efficient Fine-Tuning
Han Zhou
Xingchen Wan
Ivan Vulić
Anna Korhonen
16
45
0
28 Jan 2023
Sequential Bayesian Learning for Hidden Semi-Markov Models
Sequential Bayesian Learning for Hidden Semi-Markov Models
Patrick Aschermayr
K. Kalogeropoulos
13
0
0
25 Jan 2023
Scalable Gaussian Process Inference with Stan
Scalable Gaussian Process Inference with Stan
Till Hoffmann
J. Onnela
GP
14
4
0
21 Jan 2023
Bayesian Hierarchical Models for Counterfactual Estimation
Bayesian Hierarchical Models for Counterfactual Estimation
Natraj Raman
Daniele Magazzeni
Sameena Shah
23
5
0
21 Jan 2023
Physics-informed Information Field Theory for Modeling Physical Systems
  with Uncertainty Quantification
Physics-informed Information Field Theory for Modeling Physical Systems with Uncertainty Quantification
A. Alberts
Ilias Bilionis
32
12
0
18 Jan 2023
Bayesian Weapon System Reliability Modeling with Cox-Weibull Neural
  Network
Bayesian Weapon System Reliability Modeling with Cox-Weibull Neural Network
Michael Potter
Benny Cheng
OOD
25
3
0
04 Jan 2023
Design of Hamiltonian Monte Carlo for perfect simulation of general
  continuous distributions
Design of Hamiltonian Monte Carlo for perfect simulation of general continuous distributions
G. Leigh
Amanda R. Northrop
21
1
0
23 Dec 2022
Microcanonical Hamiltonian Monte Carlo
Microcanonical Hamiltonian Monte Carlo
Jakob Robnik
G. Luca
E. Silverstein
U. Seljak
27
14
0
16 Dec 2022
Bayesian posterior approximation with stochastic ensembles
Bayesian posterior approximation with stochastic ensembles
Oleksandr Balabanov
Bernhard Mehlig
H. Linander
BDL
UQCV
27
5
0
15 Dec 2022
Scalable Bayesian Uncertainty Quantification for Neural Network
  Potentials: Promise and Pitfalls
Scalable Bayesian Uncertainty Quantification for Neural Network Potentials: Promise and Pitfalls
Stephan Thaler
Gregor Doehner
J. Zavadlav
25
21
0
15 Dec 2022
Bridging POMDPs and Bayesian decision making for robust maintenance
  planning under model uncertainty: An application to railway systems
Bridging POMDPs and Bayesian decision making for robust maintenance planning under model uncertainty: An application to railway systems
Giacomo Arcieri
C. Hoelzl
Oliver Schwery
D. Štraub
K. Papakonstantinou
Eleni Chatzi
14
21
0
15 Dec 2022
The Ordered Matrix Dirichlet for State-Space Models
The Ordered Matrix Dirichlet for State-Space Models
Niklas Stoehr
Benjamin J. Radford
Ryan Cotterell
Aaron Schein
16
5
0
08 Dec 2022
Approximate Gibbs Sampler for Efficient Inference of Hierarchical
  Bayesian Models for Grouped Count Data
Approximate Gibbs Sampler for Efficient Inference of Hierarchical Bayesian Models for Grouped Count Data
Jin-Zhu Yu
H. Baroud
9
0
0
28 Nov 2022
Particle-based Variational Inference with Preconditioned Functional
  Gradient Flow
Particle-based Variational Inference with Preconditioned Functional Gradient Flow
Hanze Dong
Xi Wang
Yong Lin
Tong Zhang
22
19
0
25 Nov 2022
Bayesian Learning for Neural Networks: an algorithmic survey
Bayesian Learning for Neural Networks: an algorithmic survey
M. Magris
Alexandros Iosifidis
BDL
DRL
35
68
0
21 Nov 2022
Moment Propagation
Moment Propagation
J. Ormerod
Weichang Yu
11
0
0
21 Nov 2022
Unadjusted Hamiltonian MCMC with Stratified Monte Carlo Time Integration
Unadjusted Hamiltonian MCMC with Stratified Monte Carlo Time Integration
Nawaf Bou-Rabee
Milo Marsden
32
12
0
20 Nov 2022
Bayesian autoencoders for data-driven discovery of coordinates,
  governing equations and fundamental constants
Bayesian autoencoders for data-driven discovery of coordinates, governing equations and fundamental constants
Liyao (Mars) Gao
J. Nathan Kutz
AI4CE
32
20
0
19 Nov 2022
An Empirical Analysis of the Advantages of Finite- v.s. Infinite-Width
  Bayesian Neural Networks
An Empirical Analysis of the Advantages of Finite- v.s. Infinite-Width Bayesian Neural Networks
Jiayu Yao
Yaniv Yacoby
Beau Coker
Weiwei Pan
Finale Doshi-Velez
19
1
0
16 Nov 2022
Orthogonal Polynomials Approximation Algorithm (OPAA):a functional
  analytic approach to estimating probability densities
Orthogonal Polynomials Approximation Algorithm (OPAA):a functional analytic approach to estimating probability densities
Lilian W. Bialokozowicz
TPM
20
0
0
16 Nov 2022
Do Bayesian Neural Networks Need To Be Fully Stochastic?
Do Bayesian Neural Networks Need To Be Fully Stochastic?
Mrinank Sharma
Sebastian Farquhar
Eric T. Nalisnick
Tom Rainforth
BDL
16
52
0
11 Nov 2022
Bayesian score calibration for approximate models
Bayesian score calibration for approximate models
Joshua J. Bon
D. Warne
David J. Nott
Christopher C. Drovandi
29
3
0
10 Nov 2022
EEG-based performance-driven adaptive automated hazard alerting system
  in security surveillance support
EEG-based performance-driven adaptive automated hazard alerting system in security surveillance support
Xiaoshan Zhou
P-C. Liao
14
6
0
09 Nov 2022
Bayesian Neural Networks for Macroeconomic Analysis
Bayesian Neural Networks for Macroeconomic Analysis
Niko Hauzenberger
Florian Huber
K. Klieber
Massimiliano Marcellino
BDL
29
1
0
09 Nov 2022
Fully Bayesian inference for latent variable Gaussian process models
Fully Bayesian inference for latent variable Gaussian process models
Suraj Yerramilli
Akshay Iyer
Wei-Neng Chen
D. Apley
8
2
0
04 Nov 2022
A Bayesian Semiparametric Method For Estimating Causal Quantile Effects
A Bayesian Semiparametric Method For Estimating Causal Quantile Effects
Steven G. Xu
Shu Yang
Brian J. Reich
CML
8
1
0
03 Nov 2022
An optimal control perspective on diffusion-based generative modeling
An optimal control perspective on diffusion-based generative modeling
Julius Berner
Lorenz Richter
Karen Ullrich
DiffM
28
80
0
02 Nov 2022
Nonlinear System Identification: Learning while respecting physical
  models using a sequential Monte Carlo method
Nonlinear System Identification: Learning while respecting physical models using a sequential Monte Carlo method
A. Wigren
Johan Wågberg
Fredrik Lindsten
A. Wills
Thomas B. Schon
11
10
0
26 Oct 2022
Previous
123...567...161718
Next