A Differential Semantics of Lazy AR Propagation
This work addresses computational efficiency for probabilistic inference in Bayesian networks, though it appears incremental as it builds on existing LARP methods.
The paper tackles the problem of efficiently calculating partial derivatives of evidence in discrete Bayesian networks using Lazy AR Propagation (LARP), showing that the cautious LARP (cLARP) scheme provides access to a largely increased number of partial derivatives at low computational cost.
In this paper we present a differential semantics of Lazy AR Propagation (LARP) in discrete Bayesian networks. We describe how both single and multi dimensional partial derivatives of the evidence may easily be calculated from a junction tree in LARP equilibrium. We show that the simplicity of the calculations stems from the nature of LARP. Based on the differential semantics we describe how variable propagation in the LARP architecture may give access to additional partial derivatives. The cautious LARP (cLARP) scheme is derived to produce a flexible cLARP equilibrium that offers additional opportunities for calculating single and multidimensional partial derivatives of the evidence and subsets of the evidence from a single propagation. The results of an empirical evaluation illustrates how the access to a largely increased number of partial derivatives comes at a low computational cost.