  The Monte Carlo algorithm is increasingly utilized, with its central step
involving computer-based random sampling from stochastic models. While both
Markov Chain Monte Carlo (MCMC) and Reject Monte Carlo serve as sampling
methods, the latter finds fewer applications compared to the former. Hence,
this paper initially provides a concise introduction to the theory of the
Reject Monte Carlo algorithm and its implementation techniques, aiming to
enhance conceptual understanding and program implementation. Subsequently, a
simplified rejection Monte Carlo algorithm is formulated. Furthermore, by
considering multivariate distribution sampling and multivariate integration as
examples, this study explores the specific application of the algorithm in
statistical inference.
