We propose a concise function representation based on deterministic finite state automata for exact most probable explanation and constrained optimization tasks in graphical models. We then exploit our concise representation within Bucket Elimination (BE). We denote our version of BE as FABE. FABE significantly improves the performance of BE in terms of runtime and memory requirements by minimizing redundancy. Results on most probable explanation and weighted constraint satisfaction benchmarks show that FABE often outperforms the state of the art, leading to significant runtime improvements (up to 5 orders of magnitude in our tests).

翻译：我们提出一种基于确定性有限状态自动机的简洁函数表示方法，用于解决图模型中的精确最可能解释和约束优化任务。进一步地，我们将该简洁表示方法应用于桶消元算法（BE），并称改进后的版本为FABE。FABE通过最小化冗余信息，在运行时间和内存需求方面显著提升了BE的性能。在多个最可能解释和加权约束满足基准测试上的结果表明，FABE通常优于现有先进方法，带来了显著的运行时间改进（在我们的测试中最高可达五个数量级）。