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Separable equilibrium state probabilities via time reversal in Markovian process algebra

机译:马尔可夫过程代数中通过时间逆转可分离的平衡态概率

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摘要

The reversed compound agent theorem (RCAT) is a compositional result that uses Markovian process algebra (MPA) to derive the reversed process of certain interactions between two continuous time Markov chains at equilibrium. From this reversed process, together with the given, forward process, the joint state probabilities can be expressed as a product-form, although no general algorithm has previously been given. This paper first generalises RCAT to multiple (more than two) cooperating agents, which removes the need for multiple applications and inductive proofs in cooperations of an arbitrary number of processes. A new result shows a simple stochastic equivalence between cooperating, synchronised processes and corresponding parallel, asynchronous processes. This greatly simplifies the proof of the new, multi-agent theorem, which includes a statement of the desired product-form solution itself as a product of given state probabilities in the parallel components. The reversed process and product-form thus derived rely on a solution to certain rate equations and it is shown, for the first time, that a unique solution exists under mild conditions - certainly for queueing networks and G-networks.
机译:逆向复合智能定理(RCAT)是使用马尔可夫过程代数(MPA)得出平衡时两个连续时间马尔可夫链之间某些相互作用的逆向过程的合成结果。从该逆过程以及给定的正过程,可以将关节状态概率表示为乘积形式,尽管以前没有给出一般的算法。本文首先将RCAT概括为多个(两个以上)协作代理,这消除了在任意数量的过程的协作中对多个应用程序和归纳证明的需求。一个新的结果显示了协作,同步过程和相应的并行,异步过程之间的简单随机等价。这极大地简化了新的多智能体定​​理的证明,该定理包括对所需乘积形式解本身的陈述,该陈述是并行组件中给定状态概率的乘积。这样得出的逆过程和产品形式依赖于某些速率方程的解,这首次表明在温和条件下存在唯一的解-肯定是对于排队网络和G网络。

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