Finite-length Analysis on Tail probability for Markov Chain and   Application to Simple Hypothesis Testing
  Using terminologies of information geometry, we derive upper and lower bounds of the tail probability of the sample mean. Employing these bounds, we obtain upper and lower bounds of the minimum error probability of the 2nd kind of error under the exponential constraint for the error probability of the 1st kind of error in a simple hypothesis testing for a finite-length Markov chain, which yields the Hoeffding type bound. For these derivations, we derive upper and lower bounds of cumulant generating function for Markov chain. As a byproduct, we obtain another simple proof of central limit theorem for Markov chain. 
Shun Watanabe, Masahito Hayashi
math.ST cs.IT math.IT math.PR stat.TH