Abstract: Bayesian inference provides a methodology for parameter estimation and uncertainty quantification in machine learning and deep learning methods. Variational inference and Markov Chain ...

It has been proposed by E. Gelenbe in 1989. A Random Neural Network is a compose of Random Neurons and Spikes that circulates through the network. According to this model, each neuron has a positive ...

Abstract: Simulation is an excellent tool to study real-life systems with uncertainty. Discrete-event simulation (DES) is a common simulation approach to model time-dependent and complex systems.

napari is a fast, interactive, multi-dimensional image viewer for Python. It's designed for browsing, annotating, and analyzing large multi-dimensional images. It's built on top of Qt (for the GUI), ...