Ever since Pearl's probability propagation algorithm in graphs with cycles was shown to produce excellent results for error-correcting decoding a few years ago, we have been curious about whether local probability propagation could be used successfully for machine learning. One of the simplest adaptive models is the factor analyzer, which is a two-layer network that models bottom layer sensory ...

Abstract The conditional probability formula is supposed to reflect the correct updating of assignments when new information incorporated. Starting from a non-atomic measure, it proved that provides only transformed measure satisfying “minimum requirement” relational assumption. This result applies standard Bayesian parametric model.