Region Based Approximation for High Dimensional Bayesian Network Models
This addresses the problem of computational intractability in high-dimensional Bayesian Networks for researchers and practitioners, representing an incremental improvement over existing methods.
The paper tackles the challenge of intractable exact inference in densely connected Bayesian Networks by introducing the Triplet Region Construction (TRC) algorithm, which reduces clustering complexity from exponential to polynomial and achieves accurate results compared to exact solutions.
Performing efficient inference on Bayesian Networks (BNs), with large numbers of densely connected variables is challenging. With exact inference methods, such as the Junction Tree algorithm, clustering complexity can grow exponentially with the number of nodes and so computation becomes intractable. This paper presents a general purpose approximate inference algorithm called Triplet Region Construction (TRC) that reduces the clustering complexity for factorized models from worst case exponential to polynomial. We employ graph factorization to reduce connection complexity and produce clusters of limited size. Unlike MCMC algorithms TRC is guaranteed to converge and we present experiments that show that TRC achieves accurate results when compared with exact solutions.