This paper presents Markov-Chain-Monte-Carlo (MCMC) procedures to sample uniformly from the collection of datasets that satisfy some revealed preference test. The MCMC for the GARP test combines a Gibbs-sampler with a simple hit and run step. It is shown that the MCMC has the uniform distribution as its unique invariant distribution and that it converges to this distribution at an exponential rate.

中文翻译：

---

生成显示偏好一致数据集的马尔可夫链蒙特卡罗程序

本文提出了马尔可夫链蒙特卡罗 (MCMC) 程序，以从满足某些显示偏好测试的数据集集合中均匀采样。GARP 测试的 MCMC 将 Gibbs 采样器与简单的命中和运行步骤相结合。结果表明，MCMC 具有均匀分布作为其独特的不变分布，并且它以指数速率收敛到该分布。