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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
An Efficient Minibatch Acceptance Test for Metropolis-Hastings
An Efficient Minibatch Acceptance Test for Metropolis-Hastings
Daniel Seita
Xinlei Pan
Haoyu Chen
John F. Canny
42
42
0
19 Oct 2016
Black-box Importance Sampling
Black-box Importance Sampling
Qiang Liu
J. Lee
FAtt
17
73
0
17 Oct 2016
Discussion of "Fast Approximate Inference for Arbitrarily Large
  Semiparametric Regression Models via Message Passing"
Discussion of "Fast Approximate Inference for Arbitrarily Large Semiparametric Regression Models via Message Passing"
Dustin Tran
David M. Blei
19
20
0
19 Sep 2016
Relativistic Monte Carlo
Relativistic Monte Carlo
Xiaoyu Lu
Valerio Perrone
Leonard Hasenclever
Yee Whye Teh
Sebastian J. Vollmer
BDL
16
39
0
14 Sep 2016
Gray-box inference for structured Gaussian process models
Gray-box inference for structured Gaussian process models
P. Galliani
Amir Dezfouli
Edwin V. Bonilla
Novi Quadrianto
BDL
14
4
0
14 Sep 2016
Rapid Mixing of Geodesic Walks on Manifolds with Positive Curvature
Rapid Mixing of Geodesic Walks on Manifolds with Positive Curvature
Oren Mangoubi
Aaron Smith
OT
16
22
0
09 Sep 2016
Time Series Analysis of fMRI Data: Spatial Modelling and Bayesian
  Computation
Time Series Analysis of fMRI Data: Spatial Modelling and Bayesian Computation
Ming Teng
T. Johnson
F. Nathoo
11
7
0
07 Sep 2016
Generic Inference in Latent Gaussian Process Models
Generic Inference in Latent Gaussian Process Models
Edwin V. Bonilla
K. Krauth
Amir Dezfouli
BDL
15
28
0
02 Sep 2016
On the application of higher order symplectic integrators in Hamiltonian
  Monte Carlo
On the application of higher order symplectic integrators in Hamiltonian Monte Carlo
Janne Mannseth
T. S. Kleppe
H. Skaug
19
3
0
25 Aug 2016
Stein Variational Gradient Descent: A General Purpose Bayesian Inference
  Algorithm
Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm
Qiang Liu
Dilin Wang
BDL
19
1,067
0
16 Aug 2016
GPU-accelerated Gibbs sampling: a case study of the Horseshoe Probit
  model
GPU-accelerated Gibbs sampling: a case study of the Horseshoe Probit model
Alexander Terenin
Shawfeng Dong
D. Draper
11
40
0
15 Aug 2016
Cluster Sampling Filters for Non-Gaussian Data Assimilation
Cluster Sampling Filters for Non-Gaussian Data Assimilation
A. Attia
A. Moosavi
Adrian Sandu
18
20
0
13 Jul 2016
Magnetic Hamiltonian Monte Carlo
Magnetic Hamiltonian Monte Carlo
Nilesh Tripuraneni
Mark Rowland
Zoubin Ghahramani
Richard E. Turner
19
35
0
10 Jul 2016
A Common Derivation for Markov Chain Monte Carlo Algorithms with
  Tractable and Intractable Targets
A Common Derivation for Markov Chain Monte Carlo Algorithms with Tractable and Intractable Targets
K. Tran
BDL
16
2
0
07 Jul 2016
Bayesian inverse problems with $l_1$ priors: a Randomize-then-Optimize
  approach
Bayesian inverse problems with l1l_1l1​ priors: a Randomize-then-Optimize approach
Zheng Wang
Johnathan M. Bardsley
A. Solonen
Tiangang Cui
Youssef M. Marzouk
15
36
0
07 Jul 2016
Automatic Generation of Probabilistic Programming from Time Series Data
Automatic Generation of Probabilistic Programming from Time Series Data
Anh Tong
Jaesik Choi
AI4TS
6
6
0
04 Jul 2016
Swift: Compiled Inference for Probabilistic Programming Languages
Swift: Compiled Inference for Probabilistic Programming Languages
Yi Wu
Lei Li
Stuart J. Russell
Rastislav Bodík
13
29
0
30 Jun 2016
Measuring the reliability of MCMC inference with bidirectional Monte
  Carlo
Measuring the reliability of MCMC inference with bidirectional Monte Carlo
Roger C. Grosse
Siddharth Ancha
Daniel M. Roy
7
26
0
07 Jun 2016
Post-Inference Prior Swapping
Post-Inference Prior Swapping
W. Neiswanger
Eric P. Xing
12
1
0
02 Jun 2016
Merging MCMC Subposteriors through Gaussian-Process Approximations
Merging MCMC Subposteriors through Gaussian-Process Approximations
Christopher Nemeth
Chris Sherlock
16
49
0
27 May 2016
Asymptotically exact inference in differentiable generative models
Asymptotically exact inference in differentiable generative models
Matthew M. Graham
Amos J. Storkey
BDL
10
33
0
25 May 2016
Collaborative Filtering with Side Information: a Gaussian Process
  Perspective
Collaborative Filtering with Side Information: a Gaussian Process Perspective
Hyunjik Kim
Xiaoyu Lu
Seth Flaxman
Yee Whye Teh
20
3
0
23 May 2016
An Efficient and Flexible Spike Train Model via Empirical Bayes
An Efficient and Flexible Spike Train Model via Empirical Bayes
Qi She
Xiaoli Wu
Beth Jelfs
Adam S. Charles
Rosa H.M.Chan
11
0
0
10 May 2016
An Adaptive Resample-Move Algorithm for Estimating Normalizing Constants
An Adaptive Resample-Move Algorithm for Estimating Normalizing Constants
Marco Fraccaro
Ulrich Paquet
Ole Winther
17
2
0
07 Apr 2016
Variable length trajectory compressible hybrid Monte Carlo
Variable length trajectory compressible hybrid Monte Carlo
A. Nishimura
David B. Dunson
19
4
0
04 Apr 2016
Geometrically Tempered Hamiltonian Monte Carlo
Geometrically Tempered Hamiltonian Monte Carlo
A. Nishimura
David B. Dunson
8
22
0
04 Apr 2016
Debugging Machine Learning Tasks
Debugging Machine Learning Tasks
Aleksandar Chakarov
A. Nori
S. Rajamani
S. Sen
Deepak Vijaykeerthy
11
44
0
23 Mar 2016
Automatic Differentiation Variational Inference
Automatic Differentiation Variational Inference
A. Kucukelbir
Dustin Tran
Rajesh Ranganath
Andrew Gelman
David M. Blei
22
708
0
02 Mar 2016
Towards Unifying Hamiltonian Monte Carlo and Slice Sampling
Towards Unifying Hamiltonian Monte Carlo and Slice Sampling
Yizhe Zhang
Xiangyu Wang
Changyou Chen
Ricardo Henao
Kai Fan
Lawrence Carin
19
21
0
25 Feb 2016
Variational Hamiltonian Monte Carlo via Score Matching
Variational Hamiltonian Monte Carlo via Score Matching
Cheng Zhang
B. Shahbaba
Hongkai Zhao
BDL
15
26
0
06 Feb 2016
On the Geometric Ergodicity of Hamiltonian Monte Carlo
On the Geometric Ergodicity of Hamiltonian Monte Carlo
Samuel Livingstone
M. Betancourt
Simon Byrne
Mark Girolami
31
116
0
29 Jan 2016
Fitting Bayesian item response models in Stata and Stan
Fitting Bayesian item response models in Stata and Stan
Robert Grant
Daniel Furr
Bob Carpenter
Andrew Gelman
56
17
0
13 Jan 2016
Variational Inference: A Review for Statisticians
Variational Inference: A Review for Statisticians
David M. Blei
A. Kucukelbir
Jon D. McAuliffe
BDL
9
4,703
0
04 Jan 2016
Identifying the Optimal Integration Time in Hamiltonian Monte Carlo
Identifying the Optimal Integration Time in Hamiltonian Monte Carlo
M. Betancourt
8
37
0
02 Jan 2016
The Reduced-Order Hybrid Monte Carlo Sampling Smoother
The Reduced-Order Hybrid Monte Carlo Sampling Smoother
A. Attia
R. Stefanescu
Adrian Sandu
12
30
0
02 Jan 2016
Distributed Bayesian Learning with Stochastic Natural-gradient
  Expectation Propagation and the Posterior Server
Distributed Bayesian Learning with Stochastic Natural-gradient Expectation Propagation and the Posterior Server
Leonard Hasenclever
Stefan Webb
Thibaut Lienart
Sebastian J. Vollmer
Balaji Lakshminarayanan
Charles Blundell
Yee Whye Teh
BDL
16
70
0
31 Dec 2015
The Automatic Statistician: A Relational Perspective
The Automatic Statistician: A Relational Perspective
Yunseong Hwang
Anh Tong
Jaesik Choi
AI4TS
14
5
0
26 Nov 2015
Recycling intermediate steps to improve Hamiltonian Monte Carlo
Recycling intermediate steps to improve Hamiltonian Monte Carlo
A. Nishimura
David B. Dunson
22
9
0
21 Nov 2015
Model-based Dashboards for Customer Analytics
Model-based Dashboards for Customer Analytics
Ryan Dew
Asim Ansari
22
1
0
17 Nov 2015
A General Method for Robust Bayesian Modeling
A General Method for Robust Bayesian Modeling
Chong-Jun Wang
David M. Blei
OOD
7
53
0
17 Oct 2015
Pseudo-Marginal Slice Sampling
Pseudo-Marginal Slice Sampling
Iain Murray
Matthew M. Graham
18
37
0
10 Oct 2015
The Bouncy Particle Sampler: A Non-Reversible Rejection-Free Markov
  Chain Monte Carlo Method
The Bouncy Particle Sampler: A Non-Reversible Rejection-Free Markov Chain Monte Carlo Method
Alexandre Bouchard-Coté
Sebastian J. Vollmer
Arnaud Doucet
21
236
0
08 Oct 2015
A Geometric View of Posterior Approximation
A Geometric View of Posterior Approximation
T. Chen
J. Streets
B. Shahbaba
21
5
0
03 Oct 2015
Linear-time Learning on Distributions with Approximate Kernel Embeddings
Linear-time Learning on Distributions with Approximate Kernel Embeddings
Danica J. Sutherland
Junier B. Oliva
Barnabás Póczós
J. Schneider
26
17
0
24 Sep 2015
Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo
Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo
Markus Heinonen
Henrik Mannerstrom
Juho Rousu
Samuel Kaski
Harri Lähdesmäki
17
102
0
18 Aug 2015
Adaptive Multiple Importance Sampling for Gaussian Processes
Adaptive Multiple Importance Sampling for Gaussian Processes
Xiaoyu Xiong
Václav Smídl
Maurizio Filippone
32
6
0
05 Aug 2015
Metropolized Randomized Maximum Likelihood for sampling from multimodal
  distributions
Metropolized Randomized Maximum Likelihood for sampling from multimodal distributions
D. Oliver
13
30
0
30 Jul 2015
Probabilistic Programming in Python using PyMC
Probabilistic Programming in Python using PyMC
J. Salvatier
Thomas V. Wiecki
C. Fonnesbeck
GP
16
86
0
29 Jul 2015
Emulation of Higher-Order Tensors in Manifold Monte Carlo Methods for
  Bayesian Inverse Problems
Emulation of Higher-Order Tensors in Manifold Monte Carlo Methods for Bayesian Inverse Problems
Shiwei Lan
T. Bui-Thanh
M. Christie
Mark Girolami
36
56
0
22 Jul 2015
Gradient Importance Sampling
Gradient Importance Sampling
Ingmar Schuster
17
25
0
21 Jul 2015
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