Alessandro Arlotto (Duke University)
20 February 2015 @ 12:00
- Past event
Sequential decisions, time dependence, and central limit theorems
We prove a central limit theorem for the sum of functions of (1+m)-dimensional vectors from a time non-homogeneous Markov chain and we show several examples in which this central limit theorem can be used to easily establish the asymptotic normality of the optimal total reward of finite horizon Markov decision problems. By choosing m=0 we recover the classic central limit theorem of Dobrushin (1956).
Joint work with J.M. Steele.