Dr. Wei Sun Profile

Dr. Wei Sun

Research Assistant Professor

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Publications (11)

Proceedings Article | 18 May 2012 Paper

Immune allied genetic algorithm for Bayesian network structure learning

Qin Song, Feng Lin, Wei Sun, KC Chang

Proceedings Volume 8392, 839215 (2012) https://doi.org/10.1117/12.920298

KEYWORDS: Genetic algorithms, Genetics, Optimization (mathematics), Detection and tracking algorithms, Sensor fusion, Target recognition, Parallel computing, Gallium, Monte Carlo methods, Roads

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Proceedings Article | 18 May 2012 Paper

Bayesian network structure learning using chaos hybrid genetic algorithm

Jiajie Shen, Feng Lin, Wei Sun, KC Chang

Proceedings Volume 8392, 839216 (2012) https://doi.org/10.1117/12.920302

KEYWORDS: Particles, Chaos, Particle swarm optimization, Genetic algorithms, Evolutionary algorithms, Clouds, Lithium, Algorithms, Parallel computing, Optimization (mathematics)

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Proceedings Article | 12 May 2011 Paper

A new research tool for hybrid Bayesian networks using script language

Wei Sun, Cheol Young Park, Rommel Carvalho

Proceedings Volume 8050, 80501Q (2011) https://doi.org/10.1117/12.884056

KEYWORDS: Mathematics, Java, Data modeling, 3D modeling, Systems modeling, Databases, Human-machine interfaces, Algorithm development, MATLAB, Sun

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Proceedings Article | 12 May 2011 Paper

Study of the most probable explanation in hybrid Bayesian networks

Wei Sun, KC Chang

Proceedings Volume 8050, 80500T (2011) https://doi.org/10.1117/12.884039

KEYWORDS: Systems modeling, Binary data, Process modeling, Signal processing, Sun, Sensor fusion, C4I, Systems engineering, Surgery, Medical diagnostics

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Proceedings Article | 5 May 2011 Paper

Learning Bayesian network structure using a cloud-based adaptive immune genetic algorithm

Qin Song, Feng Lin, Wei Sun, KC Chang

Proceedings Volume 8050, 80500S (2011) https://doi.org/10.1117/12.883013

KEYWORDS: Clouds, Genetic algorithms, Evolutionary algorithms, Expectation maximization algorithms, Stochastic processes, Detection and tracking algorithms, Optimization (mathematics), Monte Carlo methods, Roads, C4I

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Proceedings Article | 28 April 2010 Paper

The convergence analysis of parallel genetic algorithm based on allied strategy

Feng Lin, Wei Sun, K. C. Chang

Proceedings Volume 7697, 76970P (2010) https://doi.org/10.1117/12.852046

KEYWORDS: Genetics, Genetic algorithms, Stochastic processes, Curium, Magnesium, Matrices, Optimization (mathematics), Data fusion, Thulium, Biological research

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Proceedings Article | 28 April 2010 Paper

Direct message passing for hybrid Bayesian networks and performance analysis

Wei Sun, K. C. Chang

Proceedings Volume 7697, 76970S (2010) https://doi.org/10.1117/12.852088

KEYWORDS: Monte Carlo methods, Algorithm development, Systems modeling, Error analysis, Network security, Numerical simulations, Sun, Sensor fusion, Complex systems, Analytical research

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Probabilistic inference for hybrid Bayesian networks, which involves both discrete and continuous variables, has been an important research topic over the recent years. This is not only because a number of efficient inference algorithms have been developed and used maturely for simple types of networks such as pure discrete model, but also for the practical needs that continuous variables are inevitable in modeling complex systems. Pearl's message passing algorithm provides a simple framework to compute posterior distribution by propagating messages between nodes and can provides exact answer for polytree models with pure discrete or continuous variables. In addition, applying Pearl's message passing to network with loops usually converges and results in good approximation. However, for hybrid model, there is a need of a general message passing algorithm between different types of variables. In this paper, we develop a method called Direct Message Passing (DMP) for exchanging messages between discrete and continuous variables. Based on Pearl's algorithm, we derive formulae to compute messages for variables in various dependence relationships encoded in conditional probability distributions. Mixture of Gaussian is used to represent continuous messages, with the number of mixture components up to the size of the joint state space of all discrete parents. For polytree Conditional Linear Gaussian (CLG) Bayesian network, DMP has the same computational requirements and can provide exact solution as the one obtained by the Junction Tree (JT) algorithm. However, while JT can only work for the CLG model, DMP can be applied for general nonlinear, non-Gaussian hybrid model to produce approximate solution using unscented transformation and loopy propagation. Furthermore, we can scale the algorithm by restricting the number of mixture components in the messages. Empirically, we found that the approximation errors are relatively small especially for nodes that are far away from the discrete parent nodes. Numerical simulations show encouraging results.

Proceedings Article | 17 April 2008 Paper

Convergence study of message passing in arbitrary continuous Bayesian networks

Wei Sun, K. C. Chang

Proceedings Volume 6968, 69680Z (2008) https://doi.org/10.1117/12.780626

KEYWORDS: Algorithm development, Detection and tracking algorithms, Algorithms, Statistical modeling, Stochastic processes, Sun, Systems engineering, Information fusion, Signal processing, Sensor fusion

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Proceedings Article | 25 May 2005 Paper

Probabilistic inference using linear Gaussian importance sampling for hybrid Bayesian networks

Wei Sun, K. Chang

Proceedings Volume 5809, (2005) https://doi.org/10.1117/12.606064

KEYWORDS: Stochastic processes, Performance modeling, Statistical analysis, Systems modeling, Logic, Computer simulations, Statistical modeling, Detection and tracking algorithms, Data modeling, Intelligence systems

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Proceedings Article | 9 August 2004 Paper

Performance modeling for dynamic Bayesnets networks

KuoChu Chang, Wei Sun

Proceedings Volume 5429, (2004) https://doi.org/10.1117/12.543764

KEYWORDS: Particles, Performance modeling, Monte Carlo methods, Testing and analysis, Particle filters, Computer simulations, Algorithm development, Detection and tracking algorithms, Dynamical systems, Mathematical modeling

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Proceedings Article | 25 August 2003 Paper

Comparing probabilistic inference for mixed Bayesian networks

Kuo Chu Chang, Wei Sun

Proceedings Volume 5096, (2003) https://doi.org/10.1117/12.486863

KEYWORDS: Detection and tracking algorithms, Evolutionary algorithms, Computer simulations, Artificial intelligence, Stochastic processes, Algorithm development, Target recognition, Systems modeling, MATLAB, Data fusion

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Showing 5 of 11 publications

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