Abstract: Sparse diagnosis techniques for antenna arrays provide an efficient approach to fault diagnosis by leveraging the sparse nature of faulty elements. In practical scenarios, an unknown ...

Abstract: The conventional geo-electromagnetic data inversions are mostly based on gradient optimization methods. However, this type of method can only provide a single “optimal” inverse model under ...

Bayesian methods are becoming an increasingly popular approach to data analysis across a wide range of research fields. They offer a flexible and structured framework for statistical inference, ...

Welcome to the Awesome Amortized Inference repository! This is a curated list of resources, including reviews, software, papers, and other resources related to amortized inference. Feel free to ...

As clinical drug development becomes more complex and resource-intensive, the FDA’s recent draft guidance on the use of Bayesian statistical methods in clinical trials signals a move toward more ...

The Multi-source Probabilistic Inference (MUPI) research group studies statistical machine learning and artificial intelligence. We develop new methods and algorithms for coping with uncertainty in ...

A new AI-powered framework could transform how astronomers measure the expansion of the Universe. By analyzing images of Type ...

Anupam Ojha, a postdoctoral fellow at the Flatiron Institute, develops advanced statistical frameworks to bridge the gap ...

This repository contains my solutions to the assignments in the book: "A Student’s Guide to Bayesian Statistics" by Ben Lambert. Each chapter of the book has its corresponding folder in this ...

Asteroid Vesta’s battered surface is far from static, it quietly preserves the memory of past collapses, impacts, and ...

Food System Innovations has launched an open-source Food Intelligence Lab to use AI to create better-tasting alternative ...