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 ...

A library for Bayesian neural network layers and uncertainty estimation in Deep Learning extending the core of PyTorch ...

After random selection, data from 1522 mother–child pairs were used in this study. Exposures and outcome measures Additive Bayesian network analysis was used to investigate the interdependency of ...

One of the top Python libraries for sentiment analysis is Pattern, which is a multipurpose library that can handle NLP, data mining, network analysis, machine learning, and visualization.

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) ...

This course considers the foundation of Bayesian analysis, how to use Bayesian methods in practice, and computational methods for hierarchical models. Students admitted at UiO must apply for courses ...

To mitigate the curse of dimensionality of HK for high-dimensional problems, an infinite-width Bayesian neural network correlation function is developed and incorporated to the HK model. Theoretical ...

While traditional approaches (e.g., regression models) focus on linear associations, network analysis provides unique advantages by mapping dynamic symptom interactions and identifying pivotal nodes ...

Objective The objective of this study is to evaluate the comparative benefits and harms of opioids and cannabis for medical use for chronic non-cancer pain. Design Systematic review and network ...

Design Systematic review and network meta-analysis using Bayesian analysis of randomised trials of prophylactic versus selective surfactant for RDS. Setting Cochrane Central Register of Controlled ...