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Estimation of state changes in system descriptions for dynamic Bayesian networks by using a genetic procedure and particle filters

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  • Lu, Jianjun
  • Tokinaga, Shozo

Abstract

This paper deals with the estimation of state changes in system descriptions for dynamic Bayesian networks (DBNs) by using a genetic procedure and particle filters (PFs). We extend the DBN scheme to more general cases with unknown Directed Acyclic Graph (DAG) and state changes. First, we summarize the basic model of DBN where the DAG can be changed and the state transition occurs. In the genetic procedure to estimate DAG changes, we utilize the mutation operation (called Evolutionary Programming: EP) to the DAG to maintain consistency. By defining the possible DAG structure and state changes as particles, we formalize the optimization as the PF procedure. The weight of a particle representing the DAG and state transition is defined as the capability to approximate the probability distribution function obtained from a table of cases. We apply the estimation scheme of the paper to an artificially generated DBN, in which the state of the variables and the changed structure of the DAG are already known, to prove the applicability of the method, and discuss its applicability to debt rating.

Suggested Citation

  • Lu, Jianjun & Tokinaga, Shozo, 2014. "Estimation of state changes in system descriptions for dynamic Bayesian networks by using a genetic procedure and particle filters," Economic Modelling, Elsevier, vol. 39(C), pages 138-145.
    • Handle: RePEc:eee:ecmode:v:39:y:2014:i:c:p:138-145
    DOI: 10.1016/j.econmod.2014.02.041
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    References listed on IDEAS

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    1. Jianjun Lu & Shozo Tokinaga, 2013. "Analysis of cluster formations on planer cells based on genetic programming," Computational and Mathematical Organization Theory, Springer, vol. 19(4), pages 426-445, December.
    2. Antoci, Angelo & Borghesi, Simone & Russu, Paolo, 2012. "Environmental protection mechanisms and technological dynamics," Economic Modelling, Elsevier, vol. 29(3), pages 840-847.
    3. Chi, Li-Chiu & Tang, Tseng-Chung, 2007. "Impact of reorganization announcements on distressed-stock returns," Economic Modelling, Elsevier, vol. 24(5), pages 749-767, September.
    4. Alvarez-Diaz, Marcos & Caballero Miguez, Gonzalo, 2008. "The quality of institutions: A genetic programming approach," Economic Modelling, Elsevier, vol. 25(1), pages 161-169, January.
    5. Korolkiewicz, Malgorzata W. & Elliott, Robert J., 2008. "A hidden Markov model of credit quality," Journal of Economic Dynamics and Control, Elsevier, vol. 32(12), pages 3807-3819, December.
    6. Robert Bordley & Joseph Kadane, 1999. "Experiment-dependent priors in psychology and physics," Theory and Decision, Springer, vol. 47(3), pages 213-227, December.
    7. Chen, Zhiping & Duan, Qihong, 2011. "New models of trader beliefs and their application for explaining financial bubbles," Economic Modelling, Elsevier, vol. 28(5), pages 2215-2227, September.
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    Cited by:

    1. Jianjun Lu & Shozo Tokinaga, 2016. "Cluster fluctuation in two-dimensional lattices with local interactions," Computational and Mathematical Organization Theory, Springer, vol. 22(2), pages 237-259, June.
    2. Chen, Fu-Hsiang & Chi, Der-Jang & Wang, Yi-Cheng, 2015. "Detecting biotechnology industry's earnings management using Bayesian network, principal component analysis, back propagation neural network, and decision tree," Economic Modelling, Elsevier, vol. 46(C), pages 1-10.

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