多臂Bandit过程（MAB）模型属于动态随机最优化/随机动态规划的范畴，广泛应用于精准医疗、临床试验、互联网技术，人工智能等。MAB由一组平行的可控随机过程组成，每个随机过程可以有两个选项：选择和不选择，一旦被选择，该过程就给出一个报酬流，MAB的目的是确定各个随机过程被选择和不选择的策略（时间分配），从而使得基于报酬流的一定的目标函数达到最优化。..Bayes非参数方法研究的是如何有效地对非参数统计学模型构造先验分布并进而使用使用Bayes方法进行统计学的推断。..本项目拟将非参数Bayes方法系统地引入MAB问题的研究，得到相应的最优或者一定意义上渐进最优的策略。这从理论上可以丰富MAB的研究内容次，从应用上可以为各种非参数MAB模型提供其相应的Bayes方法，从方法论的角度有可能会带来MAB决策问题的新方法和思考。

Multi-armed bandit process models lie in the domain of stochastic dynamic optimization or stochastic dynamic programming and have been extensively applied in personalized medicine, clinical trails, internet techniques, artificial intellegence and so on. An MAB consist of a collection of controllable stochastic processes, of which each allows for two options: selected and frozen and, once selected, gives rise to a flow of rewards. The objective of an MAB model is to decide the allocation of time to every member process so as to optimize certain objective functions of the overall reward flow... Bayesian nonparametrics aim at how to efficiently construct prior distributions for nonparametric statistical models so that one can make effecient statistical inference by means of Bayesian methodologies...This project intends to systematically introduce the ideas and methdologies of Bayesian nonparametrics into the research of MAB models so as to derive the policies that are optimal or asymptotically optimal in certain sense. It can enrich theoretically the content of MAB investigation, provide Bayesian analysis for the various nonparametric MAB models in the perspective of real-world applications and may give rise to new methods and insights from the point of view of methodologies.