The past decade has witnessed significant advances in causal inference and Bayesian network learning, two intertwined disciplines that allow researchers to discern underlying cause‐and‐effect ...

Bayesian networks, also known as Bayes nets, belief networks, or decision networks, are a powerful tool for understanding and reasoning about complex systems under uncertainty. They are essentially ...

The core of this patent lies in utilizing large models to handle multimodal data conflicts in chemical HSE scenarios. Its technical route can be summarized in several key steps: First, collect ...

Background Bayesian networks (BN) are directed acyclic graphs derived from empirical data that describe the dependency and probability structure. It may facilitate understanding of complex ...

Background: As quality-adjusted life years have become the standard metric in health economic evaluations, mapping health-profile or disease-specific measures onto preference-based measures to obtain ...

Latent space models are popular for analyzing dynamic network data. We propose a variational approach to estimate the model parameters and the latent positions of the nodes in the network. The ...

In the ever-evolving toolkit of statistical analysis techniques, Bayesian statistics has emerged as a popular and powerful methodology for making decisions from data in the applied sciences. Bayesian ...

Bayesian Networks, also known as Belief Networks or Bayes Nets, are a powerful probabilistic graphical model used for reasoning under uncertainty. They have been successfully applied to a wide range ...