Optimal adaptive sampling for a symmetric two-state continuous time Markov chain
研究了对称两态连续时间马尔可夫链的最优采样时间，提出一种无需参数先验知识的自适应方案，该方案在渐近意义上等价于最优固定时间设计。
We consider the optimal sampling times for a symmetric two-state continuous time Markov chain. We first consider sampling times of the form ti=iτ and find the optimal τ to minimize the asymptotic variance of our estimated parameter. This optimal τ depends upon the true unknown parameters and so it is infeasible in practice. To address this, we consider propose an adaptive scheme which we requires no knowledge of the true underlying parameter, we show that this method is asymptotically equivalent to the optimal fixed time design.