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Inconsistency of Bayesian Inference for Misspecified Linear Models, and
  a Proposal for Repairing It
v1v2v3 (latest)

Inconsistency of Bayesian Inference for Misspecified Linear Models, and a Proposal for Repairing It

11 December 2014
Peter Grünwald
T. V. Ommen
ArXiv (abs)PDFHTML

Papers citing "Inconsistency of Bayesian Inference for Misspecified Linear Models, and a Proposal for Repairing It"

48 / 148 papers shown
Title
Localization Uncertainty Estimation for Anchor-Free Object Detection
Localization Uncertainty Estimation for Anchor-Free Object Detection
Youngwan Lee
Joong-won Hwang
Hyungil Kim
Kimin Yun
Yongjin Kwon
Yuseok Bae
Joungyoul Park
94
32
0
28 Jun 2020
Uncertainty quantification using martingales for misspecified Gaussian
  processes
Uncertainty quantification using martingales for misspecified Gaussian processes
Willie Neiswanger
Aaditya Ramdas
UQCV
50
14
0
12 Jun 2020
Inject Machine Learning into Significance Test for Misspecified Linear
  Models
Inject Machine Learning into Significance Test for Misspecified Linear Models
Jiaye Teng
Yang Yuan
27
2
0
04 Jun 2020
Amortized Bayesian model comparison with evidential deep learning
Amortized Bayesian model comparison with evidential deep learning
Stefan T. Radev
Marco D’Alessandro
U. Mertens
A. Voss
Ullrich Kothe
Paul-Christian Bürkner
BDL
96
34
0
22 Apr 2020
Practical calibration of the temperature parameter in Gibbs posteriors
Practical calibration of the temperature parameter in Gibbs posteriors
Lucie Perrotta
50
3
0
22 Apr 2020
Direct loss minimization algorithms for sparse Gaussian processes
Direct loss minimization algorithms for sparse Gaussian processes
Yadi Wei
Rishit Sheth
Roni Khardon
72
14
0
07 Apr 2020
Semi-Modular Inference: enhanced learning in multi-modular models by
  tempering the influence of components
Semi-Modular Inference: enhanced learning in multi-modular models by tempering the influence of components
Chris U. Carmona
Geoff K. Nicholls
122
27
0
15 Mar 2020
Bayesian Deep Learning and a Probabilistic Perspective of Generalization
Bayesian Deep Learning and a Probabilistic Perspective of Generalization
A. Wilson
Pavel Izmailov
UQCVBDLOOD
172
657
0
20 Feb 2020
Gibbs posterior inference on multivariate quantiles
Gibbs posterior inference on multivariate quantiles
Indrabati Bhattacharya
Ryan Martin
118
21
0
03 Feb 2020
Confidence and discoveries with e-values
Confidence and discoveries with e-values
V. Vovk
Ruodu Wang
106
21
0
31 Dec 2019
Universal Inference
Universal Inference
Larry A. Wasserman
Aaditya Ramdas
Sivaraman Balakrishnan
109
148
0
24 Dec 2019
Learning under Model Misspecification: Applications to Variational and
  Ensemble methods
Learning under Model Misspecification: Applications to Variational and Ensemble methods
A. Masegosa
29
1
0
18 Dec 2019
Robust Inference and Model Criticism Using Bagged Posteriors
Robust Inference and Model Criticism Using Bagged Posteriors
Jonathan H. Huggins
Jeffrey W. Miller
142
15
0
15 Dec 2019
Diagnosing model misspecification and performing generalized Bayes'
  updates via probabilistic classifiers
Diagnosing model misspecification and performing generalized Bayes' updates via probabilistic classifiers
Owen Thomas
J. Corander
52
12
0
12 Dec 2019
Frequentist Consistency of Generalized Variational Inference
Frequentist Consistency of Generalized Variational Inference
Jeremias Knoblauch
66
11
0
10 Dec 2019
On Robust Pseudo-Bayes Estimation for the Independent Non-homogeneous
  Set-up
On Robust Pseudo-Bayes Estimation for the Independent Non-homogeneous Set-up
Tuhin Majumder
A. Basu
A. Ghosh
37
0
0
27 Nov 2019
Safe-Bayesian Generalized Linear Regression
Safe-Bayesian Generalized Linear Regression
R. D. Heide
A. Kirichenko
Nishant A. Mehta
Peter Grünwald
73
27
0
21 Oct 2019
MMD-Bayes: Robust Bayesian Estimation via Maximum Mean Discrepancy
MMD-Bayes: Robust Bayesian Estimation via Maximum Mean Discrepancy
Badr-Eddine Chérief-Abdellatif
Pierre Alquier
186
75
0
29 Sep 2019
Minimum Description Length Revisited
Minimum Description Length Revisited
Peter Grünwald
Teemu Roos
147
66
0
21 Aug 2019
Convergence Rates of Variational Inference in Sparse Deep Learning
Convergence Rates of Variational Inference in Sparse Deep Learning
Badr-Eddine Chérief-Abdellatif
BDL
122
39
0
09 Aug 2019
On the marginal likelihood and cross-validation
On the marginal likelihood and cross-validation
Edwin Fong
Chris Holmes
UQCV
138
111
0
21 May 2019
A Generalization Bound for Online Variational Inference
A Generalization Bound for Online Variational Inference
Badr-Eddine Chérief-Abdellatif
Pierre Alquier
Mohammad Emtiyaz Khan
BDL
69
27
0
08 Apr 2019
Generalized Variational Inference: Three arguments for deriving new
  Posteriors
Generalized Variational Inference: Three arguments for deriving new Posteriors
Jeremias Knoblauch
Jack Jewson
Theodoros Damoulas
DRLBDL
109
106
0
03 Apr 2019
Empirical priors for prediction in sparse high-dimensional linear
  regression
Empirical priors for prediction in sparse high-dimensional linear regression
Ryan Martin
Yiqi Tang
196
21
0
03 Mar 2019
Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning
Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning
Ruqi Zhang
Chunyuan Li
Jianyi Zhang
Changyou Chen
A. Wilson
BDL
88
278
0
11 Feb 2019
Scalable Nonparametric Sampling from Multimodal Posteriors with the
  Posterior Bootstrap
Scalable Nonparametric Sampling from Multimodal Posteriors with the Posterior Bootstrap
Edwin Fong
Simon Lyddon
Chris Holmes
184
36
0
08 Feb 2019
Gibbs posterior convergence and the thermodynamic formalism
Gibbs posterior convergence and the thermodynamic formalism
K. Mcgoff
S. Mukherjee
A. Nobel
79
10
0
24 Jan 2019
A Primer on PAC-Bayesian Learning
A Primer on PAC-Bayesian Learning
Benjamin Guedj
179
223
0
16 Jan 2019
Empirical priors and coverage of posterior credible sets in a sparse
  normal mean model
Empirical priors and coverage of posterior credible sets in a sparse normal mean model
Ryan Martin
Bo Ning
66
18
0
05 Dec 2018
Consistency of ELBO maximization for model selection
Consistency of ELBO maximization for model selection
Badr-Eddine Chérief-Abdellatif
115
19
0
28 Oct 2018
Bayesian inference in high-dimensional linear models using an empirical
  correlation-adaptive prior
Bayesian inference in high-dimensional linear models using an empirical correlation-adaptive prior
Chang-rui Liu
Yue Yang
H. Bondell
Ryan Martin
105
8
0
01 Oct 2018
Model Selection via the VC-Dimension
Model Selection via the VC-Dimension
Merlin Mpoudeu
B. Clarke
21
3
0
15 Aug 2018
Consistency of Variational Bayes Inference for Estimation and Model
  Selection in Mixtures
Consistency of Variational Bayes Inference for Estimation and Model Selection in Mixtures
Badr-Eddine Chérief-Abdellatif
Pierre Alquier
122
52
0
14 May 2018
Empirical priors and posterior concentration in a piecewise polynomial
  sequence model
Empirical priors and posterior concentration in a piecewise polynomial sequence model
Chang Liu
Ryan Martin
Weining Shen
40
6
0
11 Dec 2017
General Robust Bayes Pseudo-Posterior: Exponential Convergence results
  with Applications
General Robust Bayes Pseudo-Posterior: Exponential Convergence results with Applications
A. Ghosh
Tuhin Majumder
A. Basu
79
8
0
31 Aug 2017
Probably approximate Bayesian computation: nonasymptotic convergence of
  ABC under misspecification
Probably approximate Bayesian computation: nonasymptotic convergence of ABC under misspecification
James Ridgway
76
8
0
19 Jul 2017
Concentration of tempered posteriors and of their variational
  approximations
Concentration of tempered posteriors and of their variational approximations
Pierre Alquier
James Ridgway
119
127
0
28 Jun 2017
A Tutorial on Fisher Information
A Tutorial on Fisher Information
A. Ly
M. Marsman
J. Verhagen
R. Grasman
E. Wagenmakers
98
259
0
02 May 2017
Using stacking to average Bayesian predictive distributions
Using stacking to average Bayesian predictive distributions
Yuling Yao
Aki Vehtari
Daniel P. Simpson
Andrew Gelman
113
342
0
06 Apr 2017
Bayesian fractional posteriors
Bayesian fractional posteriors
A. Bhattacharya
D. Pati
Yun Yang
121
110
0
03 Nov 2016
Tractable Bayesian variable selection: beyond normality
Tractable Bayesian variable selection: beyond normality
D. Rossell
F. Rubio
83
31
0
06 Sep 2016
Bayesian Sparse Linear Regression with Unknown Symmetric Error
Bayesian Sparse Linear Regression with Unknown Symmetric Error
Minwoo Chae
Lizhen Lin
David B. Dunson
77
15
0
06 Aug 2016
PAC-Bayesian Theory Meets Bayesian Inference
PAC-Bayesian Theory Meets Bayesian Inference
Pascal Germain
Francis R. Bach
Alexandre Lacoste
Simon Lacoste-Julien
99
184
0
27 May 2016
Fast Rates for General Unbounded Loss Functions: from ERM to Generalized
  Bayes
Fast Rates for General Unbounded Loss Functions: from ERM to Generalized Bayes
Peter Grünwald
Nishant A. Mehta
227
73
0
01 May 2016
Data-driven priors and their posterior concentration rates
Data-driven priors and their posterior concentration rates
Ryan Martin
S. Walker
40
9
0
19 Apr 2016
Safe Probability
Safe Probability
Peter Grünwald
55
20
0
06 Apr 2016
Fast rates in statistical and online learning
Fast rates in statistical and online learning
T. Erven
Peter Grünwald
Nishant A. Mehta
Mark D. Reid
Robert C. Williamson
203
109
0
09 Jul 2015
Empirical Bayes posterior concentration in sparse high-dimensional
  linear models
Empirical Bayes posterior concentration in sparse high-dimensional linear models
Ryan Martin
Raymond Mess
S. Walker
230
104
0
30 Jun 2014
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