We design a Bayesian Learned Iterative Shrinkage-Thresholding network (BayesLIsTA). An efficient posterior inference algorithm based on probabilistic backpropagation is developed. Experiments on ...

A Bayesian network (also known as a Bayes network, Bayes net, belief network, or decision network) is a probabilistic graphical model that represents a set of variables and their conditional ...

The code demonstrates the application of Bayesian inference and probabilistic graphical modeling techniques to real-world problems in prediction and decision-making. By leveraging Bayesian Networks to ...

There have been numerous advancements in the field of generative AI and the networks used, namely autoregressive models, deep VAEs, and diffusion models. However, these models tend to have drawbacks ...

Generalized Fused Lasso for Treatment Pooling in Network Meta-Analysis; Estimation and Applications of Uncertainty in Methane Emissions Quantification Technologies: A Bayesian Approach. Evaluation of ...

Deep learning is actively used in the area of sparse coding. In current deep sparse coding methods uncertainty of predictions is rarely estimated, thus providing the results that lack the quantitative ...