EXACT ESTIMATION FOR MARKOV CHAIN EQUILIBRIUM EXPECTATIONS
EXACT ESTIMATION FOR MARKOV CHAIN EQUILIBRIUM EXPECTATIONS
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DOI：
10.1017/s0021900200021392
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发表时间：
2014-12-01
影响因子：
1
通讯作者：
Rhee, Chang-Han
中科院分区：
文献类型：
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作者：
Glynn, Peter W.;Rhee, Chang-Han
我们引入一类新的蒙特卡罗方法，我们称之为精确估计算法。这样的算法提供了无偏估计的均衡期望与实值泛函定义的马尔可夫链。我们提供了易于实现的算法的类的积极哈里斯经常性马尔可夫链，并为链的平均收缩。我们进一步认为，在马尔可夫链设置的精确估计提供了一个显着的理论放松相对于精确的模拟方法。
We introduce a new class of Monte Carlo methods, which we call exact estimation algorithms. Such algorithms provide unbiased estimators for equilibrium expectations associated with real-valued functionals defined on a Markov chain. We provide easily implemented algorithms for the class of positive Harris recurrent Markov chains, and for chains that are contracting on average. We further argue that exact estimation in the Markov chain setting provides a significant theoretical relaxation relative to exact simulation methods.