A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2001; you can also visit the original URL.
The file type is application/pdf
.
Scalable Algorithms for Adaptive Statistical Designs
2000
Scientific Programming
We present a scalable, high-performance solution to multidimensional recurrences that arise in adaptive statistical designs. Adaptive designs are an important class of learning algorithms for a stochastic environment, and we focus on the problem of optimally assigning patients to treatments in clinical trials. While adaptive designs have significant ethical and cost advantages, they are rarely utilized because of the complexity of optimizing and analyzing them. Computational challenges include
doi:10.1155/2000/508081
fatcat:ei2bjpuoqrekrivuvxbywepw44