Graph-Based Algorithms for Boolean Function Manipulation
IEEE Transactions on Computers
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in expert systems: theory and algorithms
Probabilistic reasoning in expert systems: theory and algorithms
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Importance sampling in Bayesian networks using probability trees
Computational Statistics & Data Analysis
Simulation and the Monte Carlo Method
Simulation and the Monte Carlo Method
Simulation Approaches to General Probabilistic Inference on Belief Networks
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Mixtures of deterministic-probabilistic networks and their AND/OR search space
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Mixed deterministic and probabilistic networks
Annals of Mathematics and Artificial Intelligence
Approximate counting by sampling the backtrack-free search space
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Journal of Artificial Intelligence Research
Probabilistic partial evaluation: exploiting rule structure in probabilistic inference
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Dynamic importance sampling in Bayesian networks based on probability trees
International Journal of Approximate Reasoning
SampleSearch: Importance sampling in presence of determinism
Artificial Intelligence
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
A new algorithm for sampling CSP solutions uniformly at random
CP'06 Proceedings of the 12th international conference on Principles and Practice of Constraint Programming
An importance sampling algorithm based on evidence pre-propagation
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Importance sampling algorithms for Bayesian networks: Principles and performance
Mathematical and Computer Modelling: An International Journal
| Hi-index | 0.00 |
Importance sampling is a powerful approximate inference technique for Bayesian networks. However, the performance tends to be poor when the network exhibits deterministic causalities. Deterministic causalities yield predictable influences between statistical variables. In other words, only a strict subset of the set of all variable states is permissible to sample. Samples inconsistent with the permissible state space do not contribute to the sum estimate and are effectively rejected during importance sampling. Detecting inconsistent samples is NP-hard, since it amounts to calculating the posterior probability of a sample given some evidence. Several methods have been proposed to cache inconsistent samples to improve sampling efficiency. However, cache-based methods do not effectively exploit overlap in the state patterns generated by determinism in a local network structure to compress state information. In this paper, we propose a new algorithm to reduce the overhead of caching by using an adaptive decision tree to efficiently store and detect inconsistent samples. Experimental results show that the proposed approach outperforms existing methods to sample Bayesian networks with deterministic causalities.