It is well known that standard frequentist inference breaks down in IV regressions with weak instruments. Bayesian inference with diffuse priors suffers from the same problem. We show that the issue ...

Launching a new product in today’s market isn’t just risky; it’s like setting sail into uncharted waters during a storm, with no compass and relying solely on your instincts to guide you. Throughout ...

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 ...

Sudden, hurricane-force winds toppled the luxury Bayesian superyacht that sank off the coast of Sicily last August, according to an interim report into the disaster, which found the boat had ...

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 ...

One of the central challenges in spatiotemporal prediction is efficiently handling the vast and complex datasets produced in diverse domains such as environmental monitoring, epidemiology, and cloud ...

The body of the vessel’s cook was recovered while divers searched the hull of the Bayesian for passengers, including the tech entrepreneur Mike Lynch. By Elisabetta Povoledo Deep-sea divers with Italy ...

ABSTRACT: Gender balance is a key part of the Australian identity, for creating diverse workplaces and fostering social cohesion throughout Australia. This study aims to provide a comprehensive ...

A novel Bayesian Hierarchical Network Model (BHNM) is designed for ensemble predictions of daily river stage, leveraging the spatial interdependence of river networks and hydrometeorological variables ...

Abstract: Due to the characteristics of strong suddenness, high harmfulness, and frequent occurrence of mountain flood disasters in small watersheds, the accuracy and reliability of mountain flood ...