Approximate inference for dynamic Bayesian networks: sliding window approach

Dynamic Bayesian networks (DBNs) are probabilistic graphical models that have become a ubiquitous tool for compactly describing statistical relationships among a group of stochastic processes. A suite of elaborately designed inference algorithms makes it possible for intelligent systems to use a DBN...

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Bibliographic Details
Published in:Applied intelligence (Dordrecht, Netherlands) Netherlands), 2014-06, Vol.40 (4), p.575-591
Main Authors: Gao, Xiao-Guang, Mei, Jun-Feng, Chen, Hai-Yang, Chen, Da-Qing
Format: Article
Language:English
Subjects:
Algorithmics. Computability. Computer arithmetics
Algorithms
Applied sciences
Approximation
Artificial Intelligence
Bayesian analysis
Computation
Computer Science
Computer science
control theory
systems
Dynamics
Exact sciences and technology
Inference
Information retrieval. Graph
Information systems. Data bases
Investigations
Machines
Manufacturing
Mechanical Engineering
Memory organisation. Data processing
Methods
Networks
Processes
Propagation
Sliding
Software
Stochastic models
Survival analysis
Theoretical computing
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Summary:Dynamic Bayesian networks (DBNs) are probabilistic graphical models that have become a ubiquitous tool for compactly describing statistical relationships among a group of stochastic processes. A suite of elaborately designed inference algorithms makes it possible for intelligent systems to use a DBN to make inferences in uncertain conditions. Unfortunately, exact inference or even approximation in a DBN has been proved to be NP-hard and is generally computationally prohibitive. In this paper, we investigate a sliding window framework for approximate inference in DBNs to reduce the computational burden. By introducing a sliding window that moves forward as time progresses, inference at any time is restricted to a quite narrow region of the network. The main contributions to the sliding window framework include an exploration of its foundations, explication of how it operates, and the proposal of two strategies for adaptive window size selection. To make this framework available as an inference engine, the interface algorithm widely used in exact inference is then integrated with the framework for approximate inference in DBNs. After analyzing its computational complexity, further empirical work is presented to demonstrate the validity of the proposed algorithms.
ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-013-0486-9