Bennett L. Fox Livres

The book delves into advanced techniques for quasi-Monte Carlo methods, focusing on their application in numerical integration and high-dimensional problems. It explores the theoretical foundations, including discrepancy theory and point sets, while also providing practical strategies for implementing these methods effectively. The text is enriched with examples and case studies, making it suitable for researchers and practitioners in fields like finance, engineering, and statistics who seek to enhance their computational efficiency and accuracy.

Among the significant new features are: * Markov-chain simulation (Sections 1. * better handling of asynchronous observations (Sections 3. 3 and 3. * radically updated treatment of indirect estimation (Section 3. * new section on standardized time series (Section 3. * better way to generate random integers (Section 6.