GitHub

Be Bayesian my Friend 2.0.

The course is structured in four main parts, covering the full Bayesian workflow: from probabilistic reasoning to advanced modeling. BAYESIANLEARNING/ │ ├── PART-I/ │ ├── theory/ │ │ └── ...
IEEE

Distributed Approximate Inference in State-Space Models With Heavy-Tailed Noises

Abstract: This letter addresses the distributed filtering problem, i.e., distributed approximate inference for joint state and noise statistics estimation in linear state-space models subject to heavy ...
VentureBeat

New ‘Test-Time Training’ method lets AI keep learning without exploding inference costs

A new study from researchers at Stanford University and Nvidia proposes a way for AI models to keep learning after deployment — without increasing inference costs. For enterprise agents that have to ...
GitHub

Variational inference for approximate objective priors using neural networks

In Bayesian statistics, the choice of the prior can have an important influence on the posterior and the parameter estimation, especially when few data samples are available. To limit the added ...
Frontiers

Causal inference study of PRRSV-MLV vaccine dosing effects on wean-to-finish performance during outbreaks

Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) greatly impacts swine production, and vaccination is the main method for reducing its economic effects on grow-finish populations. To cut ...
Health News Florida

COPH lab teaches students the fine points of causal inference

We all use the phrase cause and effect, but do we really know what it means and how it applies to our daily lives? Two professors in USF’s College of Public Health (COPH) are promoting the idea in ...
Frontiers

Pruning Bayesian networks for computationally tractable multi-model calibration

Anomaly response in aerospace systems increasingly relies on multi-model analysis in digital twins to replicate the system’s behaviors and inform decisions. However, computer model calibration methods ...
Scientific Research Publishing

Marin, J., Pudlo, P., Robert, C.P. and Ryder, R.J. (2011) Approximate Bayesian Computational Methods. Statistics and Computing, 22, 1167-1180.

ABSTRACT: Regression models with intractable normalizing constants are valuable tools for analyzing complex data structures, yet parameter inference for such models remains highly ...
marktechpost

This AI Paper Introduces a Machine Learning Framework to Estimate the Inference Budget for Self-Consistency and GenRMs (Generative Reward Models)

Large Language Models (LLMs) have demonstrated significant advancements in reasoning capabilities across diverse domains, including mathematics and science. However, improving these reasoning ...