Encoding probability propagation in belief networks
- Publication Type:
- Journal Article
- IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans., 2002, 32 (4), pp. 526 - 531
- Issue Date:
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
Complexity reduction is an important task in Bayesian networks. Recently, an approach known as the linear potential function (LPF) model has been proposed for approximating Bayesian computations. The LPF model can effectively compress a conditional probability table into a linear function. This correspondence extends the LPF model to approximate propagation in Bayesian networks. The extension focuses on encoding probability propagation as a polynomial function for a class of tractable problems.
Please use this identifier to cite or link to this item: