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

Learn how to create contour plots in Python using NumPy’s meshgrid and Matplotlib. This step-by-step tutorial shows you how to generate grids, compute functions over them, and visualize data ...

You can see more details about CBBA from these papers. Choi, H.-L., Brunet, L., and How, J. P., “Consensus-Based Decentralized Auctions for Robust Task Allocation ...

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.