This paper discusses the radiation response of a power supply system by combining the power converters radiation effects and the dynamic Bayesian network (DBN) model. The total ionizing dose (TID) ...

A PyTorch-based Bayesian Network framework designed to handle continuous data, enabling continuous inference with continuous CPDs. It leverages GPU acceleration and batch operations, and is intended ...

To address this challenge and enhance the efficiency of MDA based on TVAR model, we propose a Sparse Bayesian Network (SBN) that unfolds a fast Mean Field SBL (MF) using a deep variational ...

The workshop, "Efficient Approximate Bayesian Inference," drew participants from across the world. The main goals of the workshop were to enhance variational methods for analyzing complex data sets, ...

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical ...

In a surprise development, YES Network and Comcast on Monday night reached a new carriage agreement, coming to terms on a deal just hours before the regional sports network was set to go dark in ...

The NASCAR Cup Series returns to my favorite track, Martinsville Speedway, for Sunday’s Cook Out 400 (3 p.m. ET, FS1). Martinsville is a tight, half-mile circuit that inspires plenty of beating ...

The lead investors behind North America’s foremost motorsports news and information source RACER announced today that they have acquired MAVTV and will rename the TV network – that’s available on 350 ...

16 suitable variables were selected to construct a Bayesian network model. Results: The area under the curve of the unsegmented-syllabic, monosyllabic, dissyllabic, and multisyllabic training models ...