A classical problem for Markov chains is determining their stationary (or steady-state) distribution. This problem has an equally classical solution based on eigenvectors and linear equation systems. However, this approach does not scale to large instances, and iterative solutions are desirable. It turns out that a naive approach, as used by current model checkers, may yield completely wrong results. We present a new approach, which utilizes recent advances in partial exploration and mean payoff computation to obtain a correct, converging approximation.

中文翻译：

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稳态分布的正确近似

马尔可夫链的一个经典问题是确定它们的平稳（或稳态）分布。这个问题有一个同样经典的基于特征向量和线性方程组的解。但是，这种方法不能扩展到大型实例，迭代解决方案是可取的。事实证明，当前模型检查器使用的幼稚方法可能会产生完全错误的结果。我们提出了一种新方法，它利用部分探索和平均收益计算的最新进展来获得正确的收敛近似值。