A Bayesian particle Gibbs framework enables unbiased spike time inference with millisecond resolution and jointly estimates uncertainties in both spike timing and model parameters from fast calcium ...

Abstract: Bayesian inference is an important problem with numerous applications in probabilistic reasoning. Counting satisfying assignments is a closely related problem of fundamental theoretical ...

Abstract: We introduce an algorithm for Bayesian network inference using parallel computations that perform variable-elimination over multiple threads of execution. The algorithm can be implemented on ...

This article is part of the “Defending the Algorithm™” series and was written by Pittsburgh, Pennsylvania Business and IP Trial Lawyer Acacia B. Perko, Esq., with research and drafting assistance from ...

CN101 accelerates AI inference, linear algebra, and sampling workloads for diffusion models, a foundational milestone toward dramatically more energy-efficient AI. NEW YORK, Aug. 12, 2025 /PRNewswire/ ...

Quantum machine learning (QML) is an emerging research field that deals with quantum algorithms for data analysis. It is hoped that QML will yield practical demonstrations of quantum advantage by ...

Department of Intelligent Energy and Industry, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 06974, Republic of Korea Department of Intelligent Energy and Industry, Chung-Ang University, 84 ...

The FactorGraph package provides the set of different functions to perform inference over the factor graph with continuous or discrete random variables using the belief propagation algorithm.