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

May 19, 2022

Foundation Posteriors for Approximate Probabilistic Inference
Mike Wu, Noah Goodman
Zero Shot Posterior Inference Posterior Mode Variational Objective Probabilistic Program

April 6, 2022

Guaranteed Bounds for Posterior Inference in Universal Probabilistic Programming
Raven Beutner, Luke Ong, Fabian Zaiser
Posterior Inference General Bound Probabilistic Programming Probabilistic Program Probabilistic Programming Language Interval Arithmetic Interval Valued

March 18, 2022

Fast Bayesian Coresets via Subsampling and Quasi-Newton Refinement
Cian Naik, Judith Rousseau, Trevor Campbell
Posterior Inference Posterior Approximation Quasi Newton

March 17, 2022

Covid19 Reproduction Number: Credibility Intervals by Blockwise Proximal Monte Carlo Samplers
Gersende Fort, Barbara Pascal, Patrice Abry, Nelly Pustelnik
Posterior Inference Covid 19 Pandemic COVID 19 Case COVID 19 Data

February 21, 2022

Non-Volatile Memory Accelerated Posterior Estimation
Andrew Wood, Moshik Hershcovitch, Daniel Waddington, Sarel Cohen, Peter Chin
Machine Learning Model Bayesian Inference Posterior Inference Promising Long Term Storage Solution

February 15, 2022

The efficacy and generalizability of conditional GANs for posterior inference in physics-based inverse problems
Deep Ray, Harisankar Ramaswamy, Dhruv V. Patel, Assad A. Oberai
Generative Adversarial Network Multi Scale Stronger Generalizability Posterior Inference Physic Informed Conditional GANs Physic Based Inverse Problem

February 1, 2022

Deep Reference Priors: What is the best way to pretrain a model?
Yansong Gao, Rahul Ramesh, Pratik Chaudhari
Full Model Semi Supervised Learning Posterior Inference Model Based Prior Deep Prior Better Way

January 21, 2022

alpha-Deep Probabilistic Inference (alpha-DPI): efficient uncertainty quantification from exoplanet astrometry to black hole feature extraction
He Sun, Katherine L. Bouman, Paul Tiede, Jason J. Wang, Sarah Blunt, Dimitri Mawet
Uncertainty Quantification Variational Inference Posterior Inference Deep Probabilistic Model Alpha MEPOL Cosmological Inference

January 4, 2022

Quantifying Uncertainty in Deep Learning Approaches to Radio Galaxy Classification
Devina Mohan, Anna M. M. Scaife, Fiona Porter, Mike Walmsley, Micah Bowles
Deep Learning Uncertainty Quantification Variational Inference Posterior Inference Uncertainty Calibration Posterior Distribution Radio Galaxy Classification

December 30, 2021

SAE: Sequential Anchored Ensembles
Arnaud Delaunoy, Gilles Louppe
Posterior Inference Approximate Inference

December 23, 2021

December 3, 2021

Bayesian Nonparametric View to Spawning
Bahman Moraffah
Posterior Inference Bayesian Nonparametric Tractable Probabilistic Model Nonparametric Modeling

November 28, 2021

Approximate Inference via Clustering
Qianqian Song
Posterior Inference Approximate Inference Large Scale Bayesian

November 24, 2021

An MAP Estimation for Between-Class Variance
Jiao Han, Yunqi Cai, Lantian Li, Guanyu Li, Dong Wang
Speaker Verification Posterior Inference Discriminant Analysis MAP Estimation Class Variance

November 23, 2021

Weight Pruning and Uncertainty in Radio Galaxy Classification
Devina Mohan, Anna Scaife
High Uncertainty Anticipation Variational Inference Posterior Inference Uncertainty Calibration Posterior Distribution Weight Pruning Radio Galaxy Classification

November 15, 2021

Natural Gradient Variational Inference with Gaussian Mixture Models
Farzaneh Mahdisoltani
Variational Inference Gaussian Mixture Model Posterior Inference Bayesian Approach Exact Posterior Natural Gradient Variational Inference

November 12, 2021

On-the-Fly Rectification for Robust Large-Vocabulary Topic Inference
Moontae Lee, Sungjun Cho, Kun Dong, David Mimno, David Bindel
Latent Space Posterior Inference Co Occurrence Object Feature Image Rectification Topic Inference

November 9, 2021

Gaussian Process Meta Few-shot Classifier Learning via Linear Discriminant Laplace Approximation
Minyoung Kim, Timothy Hospedales
Gaussian Process Posterior Inference Shot Classification Meta Training Laplace Approximation