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 ...
Stochastic dominance provides a rigorous method to compare uncertain prospects without imposing restrictive assumptions on investor risk preferences, thus offering an alternative to traditional ...
Renewable energy is seen as an answer to climate change, yet its uptake is limited by the variability and intermittent nature of most renewable energy sources. A promising solution to this problem is ...
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 ...
Efficient execution is a significant task faced by mortgage bankers attempting to profit from the secondary market. The challenge of efficient execution is to sell or securitize a large number of ...
A first introduction to probability and statistics. This course will provide background to understand and produce rigorous statistical analysis including estimation, confidence intervals, hypothesis ...
Somer G. Anderson is CPA, doctor of accounting, and an accounting and finance professor who has been working in the accounting and finance industries for more than 20 years. Her expertise covers a ...
Many problems in quantitative finance involve both predictive forecasting and decision-based optimization. Traditionally, covariance forecasting models are optimized with unique prediction-based ...
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