Asymptotically optimal sequential discrimination between Markov chains

被引：0
|
作者：
Malyutov, MB [1 ]
Tsitovich, II [1 ]
机构：
[1] Northeastern Univ, Dept Math, Boston, MA 02115 USA
来源：
MODA6 ADVANCES IN MODEL-ORIENTED DESIGN AND ANALYSIS | 2001年
关键词：
controlled Markov chain; sequential test; second order optimality; mean length of a strategy;
D O I：
暂无
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
An asymptotic lower bound is derived involving a second additive term of order root /ln alpha/ as alpha --> 0 for the mean length of a sequential strategy 3 for discrimination between two statistical models for Markov chains. The parameter alpha is the maximal error probability of s. A sequential strategy is outlined attaining (or almost attaining) this asymptotic bound uniformly over the distributions of models including those from the indifference zone.
引用
收藏
页码：163 / 170
页数：8
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