A Comparison of Algorithms for Learning Hidden Variables in Normal Graphs
This provides explicit algorithms for rapid deployment of Bayesian graphs in applications, but it is incremental as it compares existing methods without introducing a fundamentally new approach.
The paper tackled the problem of learning hidden variables in Bayesian factor graphs by deriving localized adaptation rules from maximum likelihood and minimum KL-divergence criteria, and compared them with Viterbi-like and variational methods on synthetic data, showing performance verification across various architectures.
A Bayesian factor graph reduced to normal form consists in the interconnection of diverter units (or equal constraint units) and Single-Input/Single-Output (SISO) blocks. In this framework localized adaptation rules are explicitly derived from a constrained maximum likelihood (ML) formulation and from a minimum KL-divergence criterion using KKT conditions. The learning algorithms are compared with two other updating equations based on a Viterbi-like and on a variational approximation respectively. The performance of the various algorithm is verified on synthetic data sets for various architectures. The objective of this paper is to provide the programmer with explicit algorithms for rapid deployment of Bayesian graphs in the applications.