Google Research has proposed a training method that teaches large language models to approximate Bayesian reasoning by learning from the predictions of an optimal Bayesian system. The approach focuses ...

A patent-pending Purdue University method improves upon traditional ground-penetrating radar data to estimate the location, ...

Bayesian network structure learning using hybrid K2 search and hill climbing optimization. Discovers causal relationships in observational data across datasets with 8-50 variables and up to 10K ...

A representation of the cause-effect mechanism is needed to enable artificial intelligence to represent how the world works. Bayesian Networks (BNs) have proven to be an effective and versatile tool ...

Regression models with intractable normalizing constants are valuable tools for analyzing complex data structures, yet parameter inference for such models remains highly challenging—particularly when ...