Abstract
The goal of this software is to demonstrate the effect of the jackkniFe-bAsed neSted simulaTion (FAST) method proposed in "A FAST Method for Nested Estimation" by Guo Liang, Jun Luo and Kun Zhang. Nested estimation involves estimating an expectation of a function of a conditional expectation, and has many important applications in operations research and machine learning. However, the mean squared error (MSE) of the standard nested simulation (SNS) is only of order Γ − 2 / 3 , where Γ is the total simulation budget. Our proposed FAST method improves the convergence speed to Γ − 4 / 5 . Each folder contains a README.md file for more specific information.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
