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吸收 Markov 链的中心极限定理AI 解读

A central limit theorem is obtained for a sequence of random variables defined on a finite absorbing Markov chain, conditional on absorption not having taken place. The transition count for such a chain when suitably scaled is found to follow a multivariate normal distribution asymptotically. In the case where the transition probability matrix of the chain is a function of a single parameter α, a consistent estimator for α is found; this estimator is asymptotically normally…

吸收马尔可夫链的中心极限定理AI 解读

A central limit theorem is obtained for a sequence of random variables defined on a finite absorbing Markov chain, conditional on absorption not having taken place. The transition count for such a chain when suitably scaled is found to follow a multivariate normal distribution asymptotically. In the case where the transition probability matrix of the chain is a function of a single parameter α, a consistent estimator for α is found; this estimator is asymptotically normally…