Lyapunov conditions for differentiability of Markov chain expectations: The absolutely continuous case
arXiv: Probability(2017)
摘要
We consider a family of Markov chains whose transition dynamics are affected by model parameters. Understanding the parametric dependence of (complex) performance measures of such Markov chains is often of significant interest. The derivatives of the performance measures w.r.t. the parameters play important roles, for example, in numerical optimization of the performance measures, and quantification of the uncertainties in the performance measures when there are uncertainties in the parameters from the statistical estimation procedures. In this paper, we establish conditions that guarantee the differentiability of various types of intractable performance measures---such as the stationary and random horizon discounted performance measures---of general state space Markov chains and provide probabilistic representations for the derivatives.
更多查看译文
关键词
60J05,Markov chain,sensitivity analysis,derivative estimation
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络