Probability Estimation with Truncated Inverse Binomial Sampling
This work addresses the need for more efficient probability estimation methods in statistical and computational applications, offering a novel approach that significantly improves performance over current techniques.
The paper tackles the problem of probability estimation by developing a general theory of truncated inverse binomial sampling, which includes fixed-size and inverse binomial sampling as special cases and recovers the Chernoff-Hoeffding bound. It proposes an adaptive Monte Carlo estimation method based on this theory, achieving orders of magnitude greater efficiency compared to existing methods and software.
In this paper, we develop a general theory of truncated inverse binomial sampling. In this theory, the fixed-size sampling and inverse binomial sampling are accommodated as special cases. In particular, the classical Chernoff-Hoeffding bound is an immediate consequence of the theory. Moreover, we propose a rigorous and efficient method for probability estimation, which is an adaptive Monte Carlo estimation method based on truncated inverse binomial sampling. Our proposed method of probability estimation can be orders of magnitude more efficient as compared to existing methods in literature and widely used software.