Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Parallelizing probabilistic inference: some early explorations
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Bayesian Networks for Data Mining
Data Mining and Knowledge Discovery
A parallel Lauritzen-Spiegelhalter algorithm for probabilistic inference
Proceedings of the 1994 ACM/IEEE conference on Supercomputing
Logarithmic time parallel Bayesian inference
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
A parallel framework for loopy belief propagation
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Parallel exact inference on the cell broadband engine processor
Proceedings of the 2008 ACM/IEEE conference on Supercomputing
Modular Bayesian Network Learning for Mobile Life Understanding
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
Landmark detection from mobile life log using a modular Bayesian network model
Expert Systems with Applications: An International Journal
Preliminary findings of visualization of the interruptible moment
HPCS'09 Proceedings of the 23rd international conference on High Performance Computing Systems and Applications
Accelerating Bayesian network parameter learning using Hadoop and MapReduce
Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications
Optimizing parallel belief propagation in junction treesusing regression
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
| Hi-index | 0.00 |
We present a scalable parallel implementation for exact inference in Bayesian Networks. We explore two levels of parallelization: top level parallelization which uses pointer jumping to stride across nodes; and node level parallelization which parallelizes the node. We have implemented the algorithm using MPI and OpenMP. We consider three different types of input junction trees: linear junction trees, balanced trees and random junction trees, and obtained speedups of 203, 181 and 190 respectively over 256 processors.