\`x^2+y_1+z_12^34\`
Advanced Search
Article Contents
Article Contents

Computation of cross-moments using message passing over factor graphs

Abstract / Introduction Related Papers Cited by
  • This paper considers the problem of cross-moments computation for functions which decompose according to cycle-free factor graphs. Two algorithms are derived, both based on message passing computation of a corresponding moment-generating function ($MGF$). The first one is realized as message passing algorithm over a polynomial semiring and represents a computation of the $MGF$ Taylor coefficients, while the second one represents message passing algorithm over a binomial semiring and a computation of the $MGF$ partial derivatives. We found that some previously developed algorithms can be seen as special cases of our algorithms and we consider the time and memory complexities.
    Mathematics Subject Classification: Primary: 68W05, 94A15; Secondary: 68R01.

    Citation:

    \begin{equation} \\ \end{equation}
  • [1]

    S. Aji and R. McEliece, The generalized distributive law, IEEE Trans. Inform. Theory, 46 (2000), 325-343.doi: 10.1109/18.825794.

    [2]

    A. Azuma and Y. Matsumoto, A generalization of forward-backward algorithm, in "Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I,'' Springer-Verlag, Berlin, Heidelberg, (2009), 99-114.doi: 10.1007/978-3-642-04180-8_24.

    [3]

    C. M. Bishop, "Pattern Recognition and Machine Learning (Information Science and Statistics),'' Springer-Verlag, New York, 2006.

    [4]

    C. Cortes, M. Mohri, A. Rastogi and M. Riley, On the computation of the relative entropy of probabilistic automata, Int. J. Found. Comput. Sci., 19 (2008), 219-242.

    [5]

    R. G. Cowell, P. A. Dawid, S. L. Lauritzen and D. J. Spiegelhalter, "Probabilistic Networks and Expert Systems (Information Science and Statistics),'' Springer, New York, 2003.

    [6]

    R. Gallager, Low-density parity-check codes, IRE Trans. Inform. Theory, 8 (1962), 21-28.doi: 10.1109/TIT.1962.1057683.

    [7]

    S. Golomb, The information generating function of a probability distribution (corresp.), IEEE Trans. Inform. Theory, 12 (1966), 75-77.doi: 10.1109/TIT.1966.1053843.

    [8]

    A. Heim, V. Sidorenko and U. Sorger, Computation of distributions and their moments in the trellis, Adv. Math. Commun., 2 (2008), 373-391.doi: 10.3934/amc.2008.2.373.

    [9]

    V. M. Ilic, M. S. Stankovic and B. T. Todorovic, Entropy message passing, IEEE Trans. Inform. Theory, 57 (2011), 219-242.doi: 10.1109/TIT.2010.2090235.

    [10]

    F. Kschischang, B. Frey and H.-A. Loeliger, Factor graphs and the sum-product algorithm, IEEE Trans. Inform. Theory, 47 (2001), 498-519.doi: 10.1109/18.910572.

    [11]

    A. Kulesza and B. Taskar, Structured determinantal point processes, in "Advances in Neural Information Processing Systems 23,'' 2011.

    [12]

    Z. Li and J. Eisner, First- and second-order expectation semirings with applications to minimum-risk training on translation forests, in "Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1,'' Association for Computational Linguistics, Stroudsburg, PA, (2009), 40-51.

    [13]

    D. MacKay, Good error-correcting codes based on very sparse matrices, IEEE Trans. Inform. Theory, 45 (1999), 399-431.doi: 10.1109/18.748992.

    [14]

    D. J. C. MacKay, "Information Theory, Inference, and Learning Algorithms,'' Cambridge University Press, 2003.

    [15]

    K. P. Murphy, Y. Weiss and M. I. Jordan, Loopy belief propagation for approximate inference: an empirical study, in "Proceedings of Uncertainty in AI,'' (1999), 467-475.

    [16]

    M. Protter, "Basic Elements of Real Analysis,'' Springer, New York, 1998.

    [17]

    T. Richardson and R. Urbanke, "Modern Coding Theory,'' Cambridge University Press, 2008.doi: 10.1017/CBO9780511791338.

    [18]

    Y. Weiss, Correctness of local probability propagation in graphical models with loops, Neural Computation, 12 (2000), 1-41.doi: 10.1162/089976600300015880.

    [19]

    J. Yedidia, W. Freeman and Y. Weiss, Constructing free-energy approximations and generalized belief propagation algorithms, IEEE Trans. Inform. Theory, 51 (2005), 2282-2312.doi: 10.1109/TIT.2005.850085.

  • 加载中
SHARE

Article Metrics

HTML views() PDF downloads(117) Cited by(0)

Access History

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return