The Bayesian approach to machine learning amounts to computing posterior distributions of
random variables from a probabilistic model of how the variables are related (that is, a prior …

The probability density function of a probability distribution is a fundamental concept in
probability theory and a key ingredient in various widely used machine learning methods …

A Bayesian model is based on a pair of probability distributions, known as the prior and
sampling distributions. A wide range of fundamental machine learning tasks, including …

In order to address the rapidly increasing load of air traffic operations, innovative algorithms
and software systems must be developed for the next generation air traffic control. Extensive …

The probability density function of a probability distribution is a fundamental concept in
probability theory and a key ingredient in various widely used machine learning methods …

Modern aerospace software systems simulations usually contain many (dependent and
independent) parameters. Due to the large parameter space, and the complex, highly …

Abstract The Next Generation Air Traffic System (NGATS) is aiming to provide substantial
computer support for the air traffic controller. Algorithms for the accurate prediction of aircraft …

Industrial control systems (ICSs), especially distributed control systems (DCSs), are usually
composed of several subsystems. Each subsystem is controlled by a control unit such as a …

Graduate education presents unique challenges for both instructors and students. One
challenge is that of the graduate course enrollment numbers; wherein small size classes can …

Program synthesis systems can be highly advantageous in that users can automatically
generate code to fit a wide variety of applications from high-level specifications without …