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Computation of Kullback-Leibler Divergence Between Labeled Stochastic Systems with Non-identical State Spaces

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Algorithmic Aspects in Information and Management (AAIM 2018)

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Abstract

Model checking of biological systems is computational intensive because of state explosion. Model reduction is one of the directions that has been addressed for state explosion. Formal modeling of biological pathways leads to additional challenges given that biological pathways are multiscale and stochastic. Model abstractions incorporating multiscale biological processes are represented as labeled stochastic systems. Kullback-Leibler divergence is computed to measure the closeness of stochastic systems. A fixed point polynomial time algorithm is presented to compute Kullback-Leibler divergence with an approximation when comparing labeled stochastic systems with non-identical state spaces.

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Authors and Affiliations

  1. College of Charleston, Charleston, SC, 29401, USA

    Krishnendu Ghosh

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  1. Krishnendu Ghosh
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Correspondence to Krishnendu Ghosh .

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Editors and Affiliations

  1. The University of Texas at Dallas, Richardson, TX, USA

    Shaojie Tang

  2. University of Texas at Dallas, Richardson, TX, USA

    Ding-Zhu Du

  3. University of California, Davis, Davis, CA, USA

    David Woodruff

  4. Texas A&M University, College Station, TX, USA

    Sergiy Butenko

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Ghosh, K. (2018). Computation of Kullback-Leibler Divergence Between Labeled Stochastic Systems with Non-identical State Spaces. In: Tang, S., Du, DZ., Woodruff, D., Butenko, S. (eds) Algorithmic Aspects in Information and Management. AAIM 2018. Lecture Notes in Computer Science(), vol 11343. Springer, Cham. https://doi.org/10.1007/978-3-030-04618-7_19

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  • DOI: https://doi.org/10.1007/978-3-030-04618-7_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04617-0

  • Online ISBN: 978-3-030-04618-7

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