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A General Framework for Updating Belief Distributions
v1v2 (latest)

A General Framework for Updating Belief Distributions

27 June 2013
Pier Giovanni Bissiri
Chris Holmes
S. Walker
ArXiv (abs)PDFHTML

Papers citing "A General Framework for Updating Belief Distributions"

50 / 204 papers shown
Title
The surrogate Gibbs-posterior of a corrected stochastic MALA: Towards uncertainty quantification for neural networks
The surrogate Gibbs-posterior of a corrected stochastic MALA: Towards uncertainty quantification for neural networks
S. Bieringer
Gregor Kasieczka
Maximilian F. Steffen
Mathias Trabs
89
1
0
13 Oct 2023
Hamiltonian Dynamics of Bayesian Inference Formalised by Arc Hamiltonian
  Systems
Hamiltonian Dynamics of Bayesian Inference Formalised by Arc Hamiltonian Systems
Takuo Matsubara
33
0
0
11 Oct 2023
If there is no underfitting, there is no Cold Posterior Effect
If there is no underfitting, there is no Cold Posterior Effect
Yijie Zhang
Yi-Shan Wu
Luis A. Ortega
A. Masegosa
UQCV
66
1
0
02 Oct 2023
On the meaning of uncertainty for ethical AI: philosophy and practice
On the meaning of uncertainty for ethical AI: philosophy and practice
Cassandra Bird
Daniel Williamson
Sabina Leonelli
37
1
0
11 Sep 2023
The Interpolating Information Criterion for Overparameterized Models
The Interpolating Information Criterion for Overparameterized Models
Liam Hodgkinson
Christopher van der Heide
Roberto Salomone
Fred Roosta
Michael W. Mahoney
75
9
0
15 Jul 2023
Differentially Private Statistical Inference through $β$-Divergence
  One Posterior Sampling
Differentially Private Statistical Inference through βββ-Divergence One Posterior Sampling
Jack Jewson
Sahra Ghalebikesabi
Chris Holmes
82
2
0
11 Jul 2023
Some challenges of calibrating differentiable agent-based models
Some challenges of calibrating differentiable agent-based models
Arnau Quera-Bofarull
Joel Dyer
Anisoara Calinescu
Michael Wooldridge
AI4CE
91
7
0
03 Jul 2023
G-TRACER: Expected Sharpness Optimization
G-TRACER: Expected Sharpness Optimization
John R. Williams
Stephen J. Roberts
52
0
0
24 Jun 2023
Semiparametric posterior corrections
Semiparametric posterior corrections
Andrew Yiu
Edwin Fong
Chris Holmes
Judith Rousseau
228
4
0
09 Jun 2023
Asymptotics of Bayesian Uncertainty Estimation in Random Features
  Regression
Asymptotics of Bayesian Uncertainty Estimation in Random Features Regression
You-Hyun Baek
S. Berchuck
Sayan Mukherjee
123
0
0
06 Jun 2023
On Consistent Bayesian Inference from Synthetic Data
On Consistent Bayesian Inference from Synthetic Data
Ossi Raisa
Hibiki Ito
Antti Honkela
SyDa
102
2
0
26 May 2023
Learning Robust Statistics for Simulation-based Inference under Model
  Misspecification
Learning Robust Statistics for Simulation-based Inference under Model Misspecification
Daolang Huang
Ayush Bharti
Amauri Souza
Luigi Acerbi
Samuel Kaski
118
35
0
25 May 2023
Bayesian calibration of differentiable agent-based models
Bayesian calibration of differentiable agent-based models
Arnau Quera-Bofarull
Ayush Chopra
Anisoara Calinescu
Michael Wooldridge
Joel Dyer
63
9
0
24 May 2023
Generalized Bayesian Inference for Scientific Simulators via Amortized
  Cost Estimation
Generalized Bayesian Inference for Scientific Simulators via Amortized Cost Estimation
Richard Gao
Michael Deistler
Jakob H. Macke
133
13
0
24 May 2023
A Rigorous Link between Deep Ensembles and (Variational) Bayesian
  Methods
A Rigorous Link between Deep Ensembles and (Variational) Bayesian Methods
Veit Wild
Sahra Ghalebikesabi
Dino Sejdinovic
Jeremias Knoblauch
BDLUQCV
98
16
0
24 May 2023
Generalised likelihood profiles for models with intractable likelihoods
Generalised likelihood profiles for models with intractable likelihoods
D. Warne
Oliver J. Maclaren
E. Carr
Matthew J. Simpson
Christopher C. Drovandi
83
8
0
18 May 2023
Robustness of Bayesian ordinal response model against outliers via
  divergence approach
Robustness of Bayesian ordinal response model against outliers via divergence approach
Tomotaka Momozaki
Tomoyuki Nakagawa
60
1
0
12 May 2023
Combining experimental and observational data through a power likelihood
Combining experimental and observational data through a power likelihood
Xi Lin
J. Tarp
R. Evans
CML
63
5
0
05 Apr 2023
Robustifying likelihoods by optimistically re-weighting data
Robustifying likelihoods by optimistically re-weighting data
Miheer Dewaskar
Christopher Tosh
Jeremias Knoblauch
David B. Dunson
91
6
0
19 Mar 2023
Kernel Density Bayesian Inverse Reinforcement Learning
Kernel Density Bayesian Inverse Reinforcement Learning
Aishwarya Mandyam
Didong Li
Jiayu Yao
Diana Cai
Andrew Jones
Barbara E. Engelhardt
OffRLBDL
90
3
0
13 Mar 2023
Bayes meets Bernstein at the Meta Level: an Analysis of Fast Rates in
  Meta-Learning with PAC-Bayes
Bayes meets Bernstein at the Meta Level: an Analysis of Fast Rates in Meta-Learning with PAC-Bayes
Charles Riou
Pierre Alquier
Badr-Eddine Chérief-Abdellatif
117
10
0
23 Feb 2023
Reliable Bayesian Inference in Misspecified Models
Reliable Bayesian Inference in Misspecified Models
David T. Frazier
Robert Kohn
Christopher C. Drovandi
David Gunawan
80
5
0
13 Feb 2023
Incorporating Expert Opinion on Observable Quantities into Statistical
  Models -- A General Framework
Incorporating Expert Opinion on Observable Quantities into Statistical Models -- A General Framework
P. Cooney
Arthur J. White
28
1
0
10 Feb 2023
Robust and Scalable Bayesian Online Changepoint Detection
Robust and Scalable Bayesian Online Changepoint Detection
Matias Altamirano
F. Briol
Jeremias Knoblauch
75
14
0
09 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
83
13
0
31 Jan 2023
Semiparametric inference using fractional posteriors
Semiparametric inference using fractional posteriors
Alice L'Huillier
Luke Travis
I. Castillo
Kolyan Ray
74
5
0
19 Jan 2023
The generalized IFS Bayesian method and an associated variational
  principle covering the classical and dynamical cases
The generalized IFS Bayesian method and an associated variational principle covering the classical and dynamical cases
A. Lopes
J. Mengue
29
2
0
02 Dec 2022
A General Framework for Cutting Feedback within Modularized Bayesian
  Inference
A General Framework for Cutting Feedback within Modularized Bayesian Inference
Yang Liu
Robert J. B. Goudie
67
8
0
07 Nov 2022
Leveraging variational autoencoders for multiple data imputation
Leveraging variational autoencoders for multiple data imputation
Breeshey Roskams-Hieter
J. Wells
S. Wade
DRL
52
5
0
30 Sep 2022
How good is your Laplace approximation of the Bayesian posterior?
  Finite-sample computable error bounds for a variety of useful divergences
How good is your Laplace approximation of the Bayesian posterior? Finite-sample computable error bounds for a variety of useful divergences
Mikolaj Kasprzak
Ryan Giordano
Tamara Broderick
70
4
0
29 Sep 2022
Solving Fredholm Integral Equations of the First Kind via Wasserstein
  Gradient Flows
Solving Fredholm Integral Equations of the First Kind via Wasserstein Gradient Flows
F. R. Crucinio
Valentin De Bortoli
Arnaud Doucet
A. M. Johansen
61
3
0
16 Sep 2022
Investigating the Impact of Model Misspecification in Neural
  Simulation-based Inference
Investigating the Impact of Model Misspecification in Neural Simulation-based Inference
Patrick W Cannon
Daniel Ward
Sebastian M. Schmon
78
36
0
05 Sep 2022
Towards Reliable Simulation-Based Inference with Balanced Neural Ratio
  Estimation
Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation
Arnaud Delaunoy
Joeri Hermans
François Rozet
Antoine Wehenkel
Gilles Louppe
85
34
0
29 Aug 2022
Sampling algorithms in statistical physics: a guide for statistics and
  machine learning
Sampling algorithms in statistical physics: a guide for statistics and machine learning
Michael F Faulkner
Samuel Livingstone
57
7
0
09 Aug 2022
Tuning Stochastic Gradient Algorithms for Statistical Inference via
  Large-Sample Asymptotics
Tuning Stochastic Gradient Algorithms for Statistical Inference via Large-Sample Asymptotics
Jeffrey Negrea
Jun Yang
Haoyue Feng
Daniel M. Roy
Jonathan H. Huggins
63
1
0
25 Jul 2022
Bayesian non-conjugate regression via variational message passing
Bayesian non-conjugate regression via variational message passing
C. Castiglione
M. Bernardi
44
0
0
19 Jun 2022
Optimal quasi-Bayesian reduced rank regression with incomplete response
Optimal quasi-Bayesian reduced rank regression with incomplete response
The Tien Mai
Pierre Alquier
102
2
0
17 Jun 2022
Generalised Bayesian Inference for Discrete Intractable Likelihood
Generalised Bayesian Inference for Discrete Intractable Likelihood
Takuo Matsubara
Jeremias Knoblauch
F. Briol
Chris J. Oates
110
19
0
16 Jun 2022
Bayesian Learning of Parameterised Quantum Circuits
Bayesian Learning of Parameterised Quantum Circuits
Samuel Duffield
Marcello Benedetti
Matthias Rosenkranz
60
11
0
15 Jun 2022
Concentration of discrepancy-based approximate Bayesian computation via
  Rademacher complexity
Concentration of discrepancy-based approximate Bayesian computation via Rademacher complexity
Sirio Legramanti
Daniele Durante
Pierre Alquier
92
6
0
14 Jun 2022
Bayesian Predictive Decision Synthesis
Bayesian Predictive Decision Synthesis
Emily Tallman
M. West
66
22
0
08 Jun 2022
Deep Bootstrap for Bayesian Inference
Deep Bootstrap for Bayesian Inference
Lizhen Nie
Veronika Rockova
UQCVBDL
180
3
0
30 May 2022
Bernstein - von Mises theorem and misspecified models: a review
Bernstein - von Mises theorem and misspecified models: a review
N. Bochkina
80
9
0
28 Apr 2022
Scalable Semi-Modular Inference with Variational Meta-Posteriors
Scalable Semi-Modular Inference with Variational Meta-Posteriors
Chris U. Carmona
Geoff K. Nicholls
53
11
0
01 Apr 2022
Modularized Bayesian analyses and cutting feedback in likelihood-free
  inference
Modularized Bayesian analyses and cutting feedback in likelihood-free inference
Atlanta Chakraborty
David J. Nott
Christopher C. Drovandi
David T. Frazier
Scott A. Sisson
72
14
0
18 Mar 2022
Pitfalls of Epistemic Uncertainty Quantification through Loss
  Minimisation
Pitfalls of Epistemic Uncertainty Quantification through Loss Minimisation
Viktor Bengs
Eyke Hüllermeier
Willem Waegeman
EDLUQCVUD
90
43
0
11 Mar 2022
Cutting feedback and modularized analyses in generalized Bayesian
  inference
Cutting feedback and modularized analyses in generalized Bayesian inference
David T. Frazier
David J. Nott
92
7
0
21 Feb 2022
Robust Bayesian Inference for Simulator-based Models via the MMD
  Posterior Bootstrap
Robust Bayesian Inference for Simulator-based Models via the MMD Posterior Bootstrap
Charita Dellaporta
Jeremias Knoblauch
Theodoros Damoulas
F. Briol
88
45
0
09 Feb 2022
Variational Model Inversion Attacks
Variational Model Inversion Attacks
Kuan-Chieh Wang
Yanzhe Fu
Ke Li
Ashish Khisti
R. Zemel
Alireza Makhzani
MIACV
94
98
0
26 Jan 2022
A generalized likelihood based Bayesian approach for scalable joint
  regression and covariance selection in high dimensions
A generalized likelihood based Bayesian approach for scalable joint regression and covariance selection in high dimensions
Srijata Samanta
Kshitij Khare
George Michailidis
132
8
0
14 Jan 2022
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