本計畫首先研究由Kennedy所提出一產生(近似)貝他分配的隨機搜尋方法。其目的是找出一組最佳參數設定使Kennedy的演算法在一給定的容忍範圍內有最快的平均收歛速度。透過以下的表現值: (i)可生成的貝他分配參數範圍; (ii) 生成時間; (iii) Kolmogorov-Smirnov統計量; 及 (iv) 平均所需亂數生成數; 我們接著評估一些常用的貝他分配生成演算法。根據評估的結果,我們將提供一如何選擇最適當貝他分配生成演算法的指導方針。 In this project, we provide an overview of well-known beta algorithms. We first study a stochastic search procedure proposed by Kennedy (1988) that asymptotically generates a beta variate. The goal is to identify the optimal parameter setting so that Kennedy‘s algorithm can achieve the fastest speed of generation. For comparative purposes, we next evaluate the performance of some selected beta algorithms in terms of the following criteria: (i) validity of choice of shape parameters, (ii) computer generation time, (iii) initial set-up time, (iv) goodness-of-fit, and (v) amount of random number generation required. Based on the empirical study, we present three useful guidelines for choosing the best suited beta algorithm.