This paper surveys the use of Monte Carlo sampling-based methods for stochastic
optimization problems. Such methods are required when—as it often happens in practice …

This paper surveys the most recent advances in the context of decision making under
uncertainty, with an emphasis on the modeling of risk-averse preferences using the …

Covering formulation, algorithms, and structural results, and linking theory to real-world
applications in controlled sensing (including social learning, adaptive radars and sequential …

Optimal Transport Methods in Economics is the first textbook on the subject written
especially for students and researchers in economics. Optimal transport theory is used …

Most relevant decisions to be made by market agents within an energy market involve a
significant level of data uncertainty. For agents to make informed decisions within such a …

In this paper, we modify the Burr-XII distribution through the inverse exponential scheme to
obtain a new two-parameter distribution on the unit interval called the unit Burr-XII …

The idea for this book came from the time the authors spent at the Statistics and Applied
Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina …

An extensive update to a classic text Stochastic geometry and spatial statistics play a
fundamental role in many modern branches of physics, materials sciences, engineering …

This second edition of the popular textbook contains a comprehensive course in modern
probability theory, covering a wide variety of topics which are not usually found in …