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首页> 外文期刊>Journal of applied mathematics >Finite-State-Space Truncations for Infinite Quasi-Birth-Death Processes
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Finite-State-Space Truncations for Infinite Quasi-Birth-Death Processes

机译:有限状态空间截短无限的准出生死亡过程

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For dealing numerically with the infinite-state-space Markov chains, a truncation of the state space is inevitable, that is, an approximation by a finite-state-space Markov chain has to be performed. In this paper, we consider level-dependent quasi-birth-death processes, and we focus on the computation of stationary expectations. In previous literature, efficient methods for computing approximations to these characteristics have been suggested and established. These methods rely on truncating the process at some level N, and for N?∞, convergence of the approximation to the desired characteristic is guaranteed. This paper’s main goal is to quantify the speed of convergence. Under the assumption of an f-modulated drift condition, we derive terms for a lower bound and an upper bound on stationary expectations which converge quickly to the same value and which can be efficiently computed.
机译:对于用无限状态空间马尔可夫链数进行数值处理,状态空间的截断是不可避免的,即必须执行有限状态空间马尔可夫链的近似。在本文中,我们考虑水平依赖的准出生死亡过程,我们专注于计算静止期望的计算。在以前的文献中,已经提出并建立了用于计算这些特征的近似的有效方法。这些方法依赖于在某个级别n处截断处理,并且对于n?∞,保证了对所需特性的近似的收敛。本文的主要目标是量化收敛速度。在F调制漂移条件的假设下,我们推导出下限的术语和静止期望上的上限,这会很快收敛到相同的值,并且可以有效地计算。

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