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

Security policy: organization have complex infrastructure and need common tooling to reason about the system as a whole Protocols: expressions are a useful data type and require interoperability ...

An exercise-driven course on Advanced Python Programming that was battle-tested several hundred times on the corporate-training circuit for more than a decade. Written by David Beazley, author of the ...

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.