A Channel-based Exact Inference Algorithm for Bayesian Networks
This is an incremental improvement for researchers and practitioners in probabilistic graphical models, offering a new algorithmic approach to exact inference.
The authors tackled exact Bayesian inference by developing a new algorithm based on compositional semantics using channels, which involves linearizing the network and combining forward/backward transformations with evidence accumulation. A prototype implementation in Python showed competitive performance compared to pgmpy.
This paper describes a new algorithm for exact Bayesian inference that is based on a recently proposed compositional semantics of Bayesian networks in terms of channels. The paper concentrates on the ideas behind this algorithm, involving a linearisation (`stretching') of the Bayesian network, followed by a combination of forward state transformation and backward predicate transformation, while evidence is accumulated along the way. The performance of a prototype implementation of the algorithm in Python is briefly compared to a standard implementation (pgmpy): first results show competitive performance.