Sampling from a Discrete Distribution While Preserving Monotonicity.
NORTH CAROLINA UNIV AT CHAPEL HILL CURRICULUM IN OPERATIONS RESEARCH AND SYSTEMS ANALYSIS
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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 mn 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 - 1n. 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
- Statistics and Probability
- Operations Research