We propose a novel concise function representation for graphical models, a central theoretical framework that provides the basis for many reasoning tasks. We then show how we exploit our concise representation based on deterministic finite state automata within Bucket Elimination (BE), a general approach based on the concept of variable elimination that accommodates many inference and optimisation tasks such as most probable explanation and constrained optimisation. We denote our version of BE as FABE. By using our concise representation within FABE, we dramatically improve the performance of BE in terms of runtime and memory requirements. Results on standard benchmarks obtained using an established experimental methodology show that FABE often outperforms the best available approach (RBFAOO), leading to significant runtime improvements (up to 2 orders of magnitude in our tests).
翻译:我们为图形模型提出了一个新颖的简明功能代表,这是一个核心理论框架,为许多推理任务提供了依据。然后,我们展示了我们如何利用我们基于巴克特消除(Bucket Ereach)内部的确定性有限国家自动数据(BE)的简明表述,这是一种基于可变消除概念的一般方法,它包含许多可能的推论和优化任务,例如最有可能的解释和限制优化。我们表示我们作为FABE的版本。我们使用FABE的简明表述,极大地改进了BE在运行时间和记忆要求方面的绩效。我们采用既定实验方法得出的标准基准结果显示,FABE往往超越了现有的最佳方法(RBFAOO),导致前期的重大改进(在测试中达到2个数量级 )。