Stochastic dominance provides a rigorous method to compare uncertain prospects without imposing restrictive assumptions on investor risk preferences, thus offering an alternative to traditional ...

In this paper we study the problems of pricing and optimizing sidecar and collateralized reinsurance portfolios. The academic literature on sidecar portfolio optimization that takes into account the ...

Abstract: In the scenario-based stochastic programming problem, the solving complexity and computational burden increases as the number of scenarios increase, which involves necessary scenario ...

Course in stochastic optimization with an emphasis on formulating, solving, and approximating optimization models under uncertainty. Topics include: Models and applications: extensions of the linear ...

Professor Ruszczynski’s interests are in the theory, numerical methods and applications of stochastic optimization. He is author of "Nonlinear Optimization", "Lectures on Stochastic programming", and ...

Research areas: Healthcare optimization under uncertainty, Large-scale optimization, stochastic programming, decomposition-based integer programming algorithms ...

A first introduction to probability and statistics. This course will provide background to understand and produce rigorous statistical analysis including estimation, confidence intervals, hypothesis ...

本記事は、確率的プログラミング （Probabilistic Programming：PP）と確率的モデリング（Prorabilistic Modeling）を数式を使わずに概観する記事です。確率的プログラミングは確率的モデリングを実装する手段であり、統計、機械学習、ディープラーニング、そして ...