Posterior Inference

Posterior inference aims to estimate the probability distribution of unknown parameters given observed data, a crucial task in Bayesian statistics and machine learning. Current research heavily focuses on developing efficient algorithms, including Markov Chain Monte Carlo (MCMC) methods, variational inference (VI) techniques, and diffusion models, often combined with neural networks to handle complex, high-dimensional data. These advancements are improving the accuracy and scalability of posterior inference across diverse fields, from robotics and power systems to medical imaging and scientific modeling, enabling more robust uncertainty quantification and improved decision-making.

Papers

June 12, 2023

Conditional Matrix Flows for Gaussian Graphical Models
Marcello Massimo Negri, F. Arend Torres, Volker Roth
Variational Inference Posterior Inference Marginal Likelihood Normal Regularization Conditional F\"ollmer Flow

June 6, 2023

L-C2ST: Local Diagnostics for Posterior Approximations in Simulation-Based Inference
Julia Linhart, Alexandre Gramfort, Pedro L. C. Rodrigues
Posterior Inference Simulation Based Inference Computational Neuroscience Posterior Approximation Disease Diagnostics Flow Prior

May 31, 2023

Learning to solve Bayesian inverse problems: An amortized variational inference approach using Gaussian and Flow guides
Sharmila Karumuri, Ilias Bilionis
LeArning Abstract RSD Difference of Gaussian Posterior Inference Bayesian Inverse Problem Bayesian Posterior Amortized Variational Inference Bayesian Formulation Flow Trajectory

May 30, 2023

Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network
Tristan Deleu, Mizu Nishikawa-Toomey, Jithendaraa Subramanian, Nikolay Malkin, Laurent Charlin, Yoshua Bengio
Generative Model Many Parameter Bayesian Network Posterior Inference Generative Flow Network Marginal Distribution Joint Inference Graphical Structure

May 26, 2023

Flow Matching for Scalable Simulation-Based Inference
Maximilian Dax, Jonas Wildberger, Simon Buchholz, Stephen R. Green, Jakob H. Macke, Bernhard Schölkopf
Normalizing Flow Posterior Inference Flow Matching Simulation Based Inference Neural Posterior Estimation

May 25, 2023

Parameter Estimation in DAGs from Incomplete Data via Optimal Transport
Vy Vo, Trung Le, Tung-Long Vuong, He Zhao, Edwin Bonilla, Dinh Phung
Optimal Transport Latent Variable Posterior Inference Parameter Estimation Graphical Model Incomplete Data Variational Approximation Likelihood Estimation High Reward DAG

May 24, 2023

May 18, 2023

Posterior Inference on Shallow Infinitely Wide Bayesian Neural Networks under Weights with Unbounded Variance
Jorge Loría, Anindya Bhadra
Gaussian Process Bayesian Neural Network Posterior Inference Balancing Weight Shallow Water Unbounded Variance

May 16, 2023

Balancing Risk and Reward: An Automated Phased Release Strategy
Yufan Li, Jialiang Mao, Iavor Bojinov
Posterior Inference High Quality Risk Description Reward Report Rare Event Adaptive Bayesian

May 11, 2023

Object based Bayesian full-waveform inversion for shear elastography
Ana Carpio, Elena Cebrian, Andrea Gutierrez
Arbitrary Object Markov Chain Monte Carlo Posterior Inference Full Waveform Inversion Shear Wave

May 5, 2023

Improving Image-Based Precision Medicine with Uncertainty-Aware Causal Models
Joshua Durso-Finley, Jean-Pierre Falet, Raghav Mehta, Douglas L. Arnold, Nick Pawlowski, Tal Arbel
Uncertainty Estimation Posterior Inference Bayesian Deep Learning Uncertainty Estimation Method Precision Medicine

April 29, 2023

April 26, 2023

Mixtures of Gaussian process experts based on kernel stick-breaking processes
Yuji Saikai, Khue-Dung Dang
Gaussian Process Mixture Component Posterior Inference Mixture Model Kernel Extension Dirichlet Process

April 21, 2023

March 29, 2023

Implicit Visual Bias Mitigation by Posterior Estimate Sharpening of a Bayesian Neural Network
Rebecca S Stone, Nishant Ravikumar, Andrew J Bulpitt, David C Hogg
Bayesian Neural Network Posterior Inference Dataset Bias

March 28, 2023

Conditional Generative Models are Provably Robust: Pointwise Guarantees for Bayesian Inverse Problems
Fabian Altekrüger, Paul Hagemann, Gabriele Steidl
Inverse Problem Posterior Inference Conditional Generative Bayesian Inverse Problem Provable Guarantee Negative Log Likelihood

March 20, 2023

Bayesian Pseudo-Coresets via Contrastive Divergence
Piyush Tiwary, Kumar Shubham, Vivek V. Kashyap, Prathosh A. P
Posterior Inference COreset Selection Contrastive Divergence

Posterior Inference

Papers

Conditional Matrix Flows for Gaussian Graphical Models

L-C2ST: Local Diagnostics for Posterior Approximations in Simulation-Based Inference

Learning to solve Bayesian inverse problems: An amortized variational inference approach using Gaussian and Flow guides

Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network

Flow Matching for Scalable Simulation-Based Inference

Parameter Estimation in DAGs from Incomplete Data via Optimal Transport

Generalized Bayesian Inference for Scientific Simulators via Amortized Cost Estimation

Masked Bayesian Neural Networks : Theoretical Guarantee and its Posterior Inference

Posterior Inference on Shallow Infinitely Wide Bayesian Neural Networks under Weights with Unbounded Variance

Balancing Risk and Reward: An Automated Phased Release Strategy

Object based Bayesian full-waveform inversion for shear elastography

Improving Image-Based Precision Medicine with Uncertainty-Aware Causal Models

Representing Additive Gaussian Processes by Sparse Matrices

EBLIME: Enhanced Bayesian Local Interpretable Model-agnostic Explanations

Mixtures of Gaussian process experts based on kernel stick-breaking processes

Machine Learning and the Future of Bayesian Computation

Plug-and-Play split Gibbs sampler: embedding deep generative priors in Bayesian inference

Implicit Visual Bias Mitigation by Posterior Estimate Sharpening of a Bayesian Neural Network

Conditional Generative Models are Provably Robust: Pointwise Guarantees for Bayesian Inverse Problems

Bayesian Pseudo-Coresets via Contrastive Divergence