Advances in Bayesian methods for big data

In the Big Data era, many scientific and engineering domains are producing massive data streams, with petabyte and exabyte scales becoming increasingly common. Besides the explosive growth in volume, Big Data also has high velocity, high variety, and high uncertainty. These complex data streams require ever-increasing processing speeds, economical storage, and timely response for decision making in highly uncertain environments, and have raised various challenges to conventional data analysis. With the primary goal of building intelligent systems that automatically improve from experiences, machine learning (ML) is becoming an increasingly important field to tackle big data challenges, with an emerging field of "Big Learning," which covers theories, algorithms and systems on addressing big data problems. Bayesian methods have been widely used in machine learning and many other areas.