Sampling from a Discrete Distribution While Preserving Monotonicity.
Abstract
This paper describes a cutpoint method for sampling from an n-point discrete distribution that preserves the monotone relationship between a uniform deviate and the random variate it generates. This property is useful when developing a sampling plan to reduce variance in a Monte Carlo or simulation study. The alias sampling method generally lacks this property and requires 2n storage locations while the proposed cutpoint sampling method requires m+n storage locations, where m donotes the number of cutpoints. The expected number of comparisons with this method is derived and shown to be bounded above by (m + n - 1/n. The paper describes an algorithm to implement the proposed method as well as two modifications for cases in which n is large and possibly infinite. (Author)
Document Details
- Document Type
- Technical Report
- Publication Date
- Feb 01, 1982
- Accession Number
- ADA111967
Entities
People
- George S. Fishman
- Louis R. Moore Iii
Organizations
- University of North Carolina at Chapel Hill