A two-stage framework that corrects coarse weather forecasts before predicting solar output halved day-ahead power forecast ...

Researchers have introduced new dimension and model reduction methods that extend optimal Bayesian inference to problems with ...

Probabilities that created rebels, hidden signatures, unrecorded drivers, inbox gatekeepers, and those who predict the ...

Understanding Bayesian inference through a drinking invitationHow does probability change with new information?Today's goal:Understand Bayesian inference using familiar problems and connect it to ...

A Bayesian VAR that forecasts the US macro-financial system, and a stress-test scenario generator built on top of it. The model estimates how eleven US variables move together, output, unemployment, ...

This webinar explores how advanced Bayesian forecasting models can improve predictions of labour market trends and support more informed policy decisions. Bayesian methods are being increasingly used ...

Researchers from the School of Intelligent Systems Engineering at Sun Yat-sen University in Shenzhen, along with the Guangdong Provincial Key Laboratory of Intelligent Transportation System and the ...

Abstract: Wind power forecasting plays an increasingly important role on the safety, reliability, and stability of the power grid. Due to the fluctuating and intermittent nature of wind resources, it ...

Abstract: This article presents a novel deep learning model, the Attentive Bayesian Multi-Stage Forecasting Network (ABMF-Net), designed for robust forecasting of electricity price (USD/MWh) and ...

An official report into last year’s yacht tragedy, which killed seven, found that the boat could easily capsize in high winds. Its towering mast made it more vulnerable. By Jeffrey Gettleman and James ...

ABSTRACT: This paper examines the forecasting of the Consumer Price Index (CPI) and Producer Price Index (PPI) using commodity price index and crude oil price, focusing on a comparison between China ...