Implementation of bayesian non-parametric inference based on beta processes
本文针对Hjort提出的贝塔过程，开发了一种利用莱维公式从后验过程中生成近似随机变量的算法，并用失效时间数据演示计算。
Hjort (1990) constructs prior distributions for cumulative hazards using stochastic processes with non-negative independent increments. A particular class of processes termed beta processes is introduced there for this purpose. It is also shown that the posterior cumulative hazard is again a beta process given exact and right censored data. In this paper, we develop an algorithm that enables approximate random variate generation from the posterior process using the Levy formula for its moment generating function. Computation is illustrated using failure time data.