Robust And Distributed Hypothesis Testing

This book generalizes and extends the available theory in robust and decentralized hypothesis testing. In particular, it presents a robust test for modeling errors which is independent from the assumptions that a sufficiently large number of samples is available, and that the distance is the KL-divergence. Here,

This book generalizes and extends the available theory in robust and decentralized hypothesis testing. In particular, it presents a robust test for modeling errors which is independent from the assumptions that a sufficiently large number of samples is available, and that the distance is the KL-divergence. Here, the distance can be chosen from a much general model,

Building on the Foundation: Binomial & Normal Distribution, CRISP DM, Anova, Matrices, Coordinate Geometry, Calculus.

Master Risk Management Essentials and Earn Your Certified Associate Professional Risk Manager Credential

Master Risk Management Essentials and Earn Your Certified Associate Professional Risk Manager Credential

Master the essential skills and tools for Business Analytics to excel in data-driven decision-making.