Abstract: Due to the difficulty of reliability analysis of multistate systems, a new method based on Bayesian networks is proposed through an example. Reliability block diagram and logic operators are ...

Bayesian-Torch is a library of neural network layers and utilities extending the core of PyTorch to enable Bayesian inference in deep learning models to quantify principled uncertainty estimates in ...

Bayesian regression with linear basis function models. Introduction to Bayesian linear regression. Implementation with plain NumPy and scikit-learn. See also PyMC3 implementation. Gaussian processes.

Perceptual judgments of ambiguous stimuli are often biased by prior expectations. These biases may offer a window into the neural computations that give rise to perceptual interpretations of the ...

Understanding the interplay between network architecture, dataset statistics, and learning algorithms is a key challenge in deep learning. We overcome this challenge analytically for zero-noise ...

Abstract: As for the uncertainty and complex association in the fault diagnosis of AUV system, a method based on Bayesian networks is proposed, which is applied into fault diagnosis of AUV. By the ...

Different data sources can provide complementary information. Moving from a simple approach based on using one data source at a time to a systems approach that integrates multiple data sources ...

Purpose: Bayesian calibration is generally superior to standard direct-search algorithms in that it estimates the full joint posterior distribution of the calibrated parameters. However, there are ...

Don’t worry, a little Bayesian analysis won’t hurt you. By Siobhan Roberts There is a statistician’s rejoinder — sometimes offered as wry criticism, sometimes as honest advice — that could hardly be a ...