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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
Verifying Inverse Model Neural Networks
Verifying Inverse Model Neural Networks
Chelsea Sidrane
Sydney M. Katz
Anthony Corso
Mykel J. Kochenderfer
19
2
0
04 Feb 2022
Sampling with Riemannian Hamiltonian Monte Carlo in a Constrained Space
Sampling with Riemannian Hamiltonian Monte Carlo in a Constrained Space
Yunbum Kook
Y. Lee
Ruoqi Shen
Santosh Vempala
11
38
0
03 Feb 2022
Lagrangian Manifold Monte Carlo on Monge Patches
Lagrangian Manifold Monte Carlo on Monge Patches
M. Hartmann
Mark Girolami
Arto Klami
18
10
0
01 Feb 2022
Bayesian Nonlinear Models for Repeated Measurement Data: An Overview,
  Implementation, and Applications
Bayesian Nonlinear Models for Repeated Measurement Data: An Overview, Implementation, and Applications
Se Yoon Lee
17
18
0
28 Jan 2022
Spatial meshing for general Bayesian multivariate models
Spatial meshing for general Bayesian multivariate models
M. Peruzzi
David B. Dunson
90
6
0
25 Jan 2022
Geometrically adapted Langevin dynamics for Markov chain Monte Carlo
  simulations
Geometrically adapted Langevin dynamics for Markov chain Monte Carlo simulations
Mariya Mamajiwala
D. Roy
S. Guillas
11
0
0
20 Jan 2022
Accelerating Bayesian inference of dependency between complex biological
  traits
Accelerating Bayesian inference of dependency between complex biological traits
Zhenyu Zhang
A. Nishimura
Nídia S. Trovão
Joshua L. Cherry
Andrew J Holbrook
Xiang Ji
P. Lemey
M. Suchard
20
2
0
18 Jan 2022
Bayesian Calibration of Imperfect Computer Models using Physics-Informed
  Priors
Bayesian Calibration of Imperfect Computer Models using Physics-Informed Priors
Michail Spitieris
I. Steinsland
AI4CE
20
6
0
17 Jan 2022
Loss-calibrated expectation propagation for approximate Bayesian
  decision-making
Loss-calibrated expectation propagation for approximate Bayesian decision-making
Michael J. Morais
Jonathan W. Pillow
44
6
0
10 Jan 2022
Bayesian Trend Filtering via Proximal Markov Chain Monte Carlo
Bayesian Trend Filtering via Proximal Markov Chain Monte Carlo
Qiang Heng
Hua Zhou
Eric C. Chi
16
9
0
01 Jan 2022
Surrogate Likelihoods for Variational Annealed Importance Sampling
Surrogate Likelihoods for Variational Annealed Importance Sampling
M. Jankowiak
Du Phan
BDL
35
13
0
22 Dec 2021
Transformers Can Do Bayesian Inference
Transformers Can Do Bayesian Inference
Samuel G. Müller
Noah Hollmann
Sebastian Pineda Arango
Josif Grabocka
Frank Hutter
BDL
UQCV
22
140
0
20 Dec 2021
The Apogee to Apogee Path Sampler
The Apogee to Apogee Path Sampler
Chris Sherlock
S. Urbas
Matthew Ludkin
24
6
0
15 Dec 2021
Fast characterization of inducible regions of atrial fibrillation models
  with multi-fidelity Gaussian process classification
Fast characterization of inducible regions of atrial fibrillation models with multi-fidelity Gaussian process classification
Lia Gander
Simone Pezzuto
A. Gharaviri
Rolf Krause
P. Perdikaris
F. Sahli Costabal
8
13
0
15 Dec 2021
hIPPYlib-MUQ: A Bayesian Inference Software Framework for Integration of
  Data with Complex Predictive Models under Uncertainty
hIPPYlib-MUQ: A Bayesian Inference Software Framework for Integration of Data with Complex Predictive Models under Uncertainty
Ki-tae Kim
Umberto Villa
M. Parno
Youssef Marzouk
Omar Ghattas
N. Petra
25
18
0
01 Dec 2021
Querying Labelled Data with Scenario Programs for Sim-to-Real Validation
Querying Labelled Data with Scenario Programs for Sim-to-Real Validation
Edward Kim
Jay Shenoy
Sebastian Junges
Daniel J. Fremont
Alberto L. Sangiovanni-Vincentelli
S. Seshia
38
3
0
01 Dec 2021
Path Integral Sampler: a stochastic control approach for sampling
Path Integral Sampler: a stochastic control approach for sampling
Qinsheng Zhang
Yongxin Chen
DiffM
18
101
0
30 Nov 2021
Differentially private stochastic expectation propagation (DP-SEP)
Differentially private stochastic expectation propagation (DP-SEP)
Margarita Vinaroz
Mijung Park
25
1
0
25 Nov 2021
Bootstrap Your Flow
Bootstrap Your Flow
Laurence Illing Midgley
Vincent Stimper
G. Simm
José Miguel Hernández-Lobato
25
5
0
22 Nov 2021
Functional Model of Residential Consumption Elasticity under Dynamic
  Tariffs
Functional Model of Residential Consumption Elasticity under Dynamic Tariffs
K. Ganesan
Joao Tomé Saraiva
R. Bessa
9
8
0
22 Nov 2021
On Numerical Considerations for Riemannian Manifold Hamiltonian Monte
  Carlo
On Numerical Considerations for Riemannian Manifold Hamiltonian Monte Carlo
James A. Brofos
Roy R. Lederman
16
8
0
19 Nov 2021
The Ball Pit Algorithm: A Markov Chain Monte Carlo Method Based on Path
  Integrals
The Ball Pit Algorithm: A Markov Chain Monte Carlo Method Based on Path Integrals
Miguel Fudolig
Réka Howard
24
0
0
05 Nov 2021
Local-Global MCMC kernels: the best of both worlds
Local-Global MCMC kernels: the best of both worlds
S. Samsonov
E. Lagutin
Marylou Gabrié
Alain Durmus
A. Naumov
Eric Moulines
19
13
0
04 Nov 2021
Hamiltonian Dynamics with Non-Newtonian Momentum for Rapid Sampling
Hamiltonian Dynamics with Non-Newtonian Momentum for Rapid Sampling
Greg Ver Steeg
Aram Galstyan
41
13
0
03 Nov 2021
Likelihood-Free Inference in State-Space Models with Unknown Dynamics
Likelihood-Free Inference in State-Space Models with Unknown Dynamics
Alexander Aushev
Thong Tran
Henri Pesonen
Andrew Howes
Samuel Kaski
28
1
0
02 Nov 2021
Learning Size and Shape of Calabi-Yau Spaces
Learning Size and Shape of Calabi-Yau Spaces
Magdalena Larfors
A. Lukas
Fabian Ruehle
Robin Schneider
6
41
0
02 Nov 2021
Efficient Learning of the Parameters of Non-Linear Models using
  Differentiable Resampling in Particle Filters
Efficient Learning of the Parameters of Non-Linear Models using Differentiable Resampling in Particle Filters
Conor Rosato
Vincent Beraud
P. Horridge
Thomas B. Schon
Simon Maskell
18
14
0
02 Nov 2021
Entropy-based adaptive Hamiltonian Monte Carlo
Entropy-based adaptive Hamiltonian Monte Carlo
Marcel Hirt
Michalis K. Titsias
P. Dellaportas
BDL
37
7
0
27 Oct 2021
Locally Differentially Private Bayesian Inference
Locally Differentially Private Bayesian Inference
Tejas D. Kulkarni
Joonas Jälkö
Samuel Kaski
Antti Honkela
48
2
0
27 Oct 2021
Epidemia: An R Package for Semi-Mechanistic Bayesian Modelling of
  Infectious Diseases using Point Processes
Epidemia: An R Package for Semi-Mechanistic Bayesian Modelling of Infectious Diseases using Point Processes
Shuhang Tan
Axel Gandy
Swapnil Mishra
Samir Bhatt
Seth Flaxman
H. Juliette T. Unwin
J. Ish-Horowicz
11
9
0
24 Oct 2021
Focusing on Difficult Directions for Learning HMC Trajectory Lengths
Focusing on Difficult Directions for Learning HMC Trajectory Lengths
Pavel Sountsov
Matt Hoffman
29
9
0
22 Oct 2021
Interpolating between sampling and variational inference with infinite
  stochastic mixtures
Interpolating between sampling and variational inference with infinite stochastic mixtures
Richard D. Lange
Ari S. Benjamin
Ralf M. Haefner
Xaq Pitkow
11
7
0
18 Oct 2021
Fast and Scalable Inference for Spatial Extreme Value Models
Fast and Scalable Inference for Spatial Extreme Value Models
Mei-Ching Chen
R. Ramezan
Martin Lysy
27
1
0
13 Oct 2021
The Neural Testbed: Evaluating Joint Predictions
The Neural Testbed: Evaluating Joint Predictions
Ian Osband
Zheng Wen
S. Asghari
Vikranth Dwaracherla
Botao Hao
M. Ibrahimi
Dieterich Lawson
Xiuyuan Lu
Brendan O'Donoghue
Benjamin Van Roy
UQCV
29
20
0
09 Oct 2021
An Uncertainty-aware Loss Function for Training Neural Networks with
  Calibrated Predictions
An Uncertainty-aware Loss Function for Training Neural Networks with Calibrated Predictions
Afshar Shamsi
Hamzeh Asgharnezhad
AmirReza Tajally
Saeid Nahavandi
Henry Leung
UQCV
44
6
0
07 Oct 2021
Fast methods for posterior inference of two-group normal-normal models
Fast methods for posterior inference of two-group normal-normal models
P. Greengard
J. Hoskins
Charles C.Margossian
Andrew Gelman
Aki Vehtari
23
1
0
06 Oct 2021
Relative Entropy Gradient Sampler for Unnormalized Distributions
Relative Entropy Gradient Sampler for Unnormalized Distributions
Xingdong Feng
Yuan Gao
Jian Huang
Yuling Jiao
Xu Liu
33
7
0
06 Oct 2021
Kalman Bayesian Neural Networks for Closed-form Online Learning
Kalman Bayesian Neural Networks for Closed-form Online Learning
Philipp Wagner
Xinyang Wu
Marco F. Huber
BDL
17
12
0
03 Oct 2021
Delayed rejection Hamiltonian Monte Carlo for sampling multiscale
  distributions
Delayed rejection Hamiltonian Monte Carlo for sampling multiscale distributions
Chirag Modi
A. Barnett
Bob Carpenter
36
14
0
01 Oct 2021
TyXe: Pyro-based Bayesian neural nets for Pytorch
TyXe: Pyro-based Bayesian neural nets for Pytorch
H. Ritter
Theofanis Karaletsos
OOD
MU
BDL
26
6
0
01 Oct 2021
Combining Human Predictions with Model Probabilities via Confusion
  Matrices and Calibration
Combining Human Predictions with Model Probabilities via Confusion Matrices and Calibration
Gavin Kerrigan
Padhraic Smyth
M. Steyvers
27
46
0
29 Sep 2021
Nested Sampling for Non-Gaussian Inference in SLAM Factor Graphs
Nested Sampling for Non-Gaussian Inference in SLAM Factor Graphs
Qiangqiang Huang
Alan Papalia
J. Leonard
29
4
0
22 Sep 2021
Probabilistic Inference of Simulation Parameters via Parallel
  Differentiable Simulation
Probabilistic Inference of Simulation Parameters via Parallel Differentiable Simulation
Eric Heiden
Chris Denniston
David Millard
Fabio Ramos
Gaurav Sukhatme
27
21
0
18 Sep 2021
Load Balancing in Compute Clusters with Delayed Feedback
Load Balancing in Compute Clusters with Delayed Feedback
Anam Tahir
Bastian Alt
Amr Rizk
Heinz Koeppl
14
1
0
17 Sep 2021
LSB: Local Self-Balancing MCMC in Discrete Spaces
LSB: Local Self-Balancing MCMC in Discrete Spaces
Emanuele Sansone
9
10
0
08 Sep 2021
Dependent Dirichlet Processes for Analysis of a Generalized Shared
  Frailty Model
Dependent Dirichlet Processes for Analysis of a Generalized Shared Frailty Model
Chong Zhong
Zhihua Ma
Junshan Shen
Catherine C. Liu
11
0
0
08 Sep 2021
Gaussian Process Uniform Error Bounds with Unknown Hyperparameters for
  Safety-Critical Applications
Gaussian Process Uniform Error Bounds with Unknown Hyperparameters for Safety-Critical Applications
A. Capone
Armin Lederer
Sandra Hirche
24
18
0
06 Sep 2021
Variational Inference with NoFAS: Normalizing Flow with Adaptive
  Surrogate for Computationally Expensive Models
Variational Inference with NoFAS: Normalizing Flow with Adaptive Surrogate for Computationally Expensive Models
Yu Wang
F. Liu
Daniele E. Schiavazzi
TPM
BDL
12
14
0
28 Aug 2021
Modeling Item Response Theory with Stochastic Variational Inference
Modeling Item Response Theory with Stochastic Variational Inference
Mike Wu
R. Davis
B. Domingue
Chris Piech
Noah D. Goodman
CML
20
5
0
26 Aug 2021
Pathfinder: Parallel quasi-Newton variational inference
Pathfinder: Parallel quasi-Newton variational inference
Lu Zhang
Bob Carpenter
A. Gelman
Aki Vehtari
43
40
0
09 Aug 2021
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