BEAL is a deep active learning method that uses Bayesian deep learning with dropout to infer the model’s posterior predictive distribution and introduces an expected confidence-based acquisition function to select uncertain samples. Experiments show that BEAL outperforms other active learning methods, requiring fewer labeled samples for efficient training.
This guide walks through applications, libraries, and dependencies of causal discovery approaches using Bayesian modeling, with a step-by-step guide on creating causal networks using discrete or continuous datasets, explaining techniques and search methods like PC and Hill Climb Search, ensuring readers understand Bayesian techniques for causal discovery in specific use cases."
This article introduces the Bayesian Neural Field (BayesNF), a method combining deep neural networks with hierarchical Bayesian inference for scalable and flexible analysis of spatiotemporal data, such as environmental monitoring and cloud demand forecasting.
A deep dive into the theory and applications of diffusion models, focusing on image generation and other tasks, with examples and PyTorch code.
A hands-on tutorial in Python for sensor engineers on Bayesian sensor calibration, which combines statistical models and data to optimally calibrate sensors. This technique is crucial in engineering to minimize sensor measurement uncertainty. The tutorial provides Python code to perform such calibration numerically using existing libraries.
Dirichlet is a conjugate prior to the Multinomial likelihood. In other words our posterior distr. is also a Dirichlet distributuion with parameters incorporating observed data.
