Abstract
In this paper we analyse the problem of probabilistic inference in CLG networks when evidence comes in streams. In such situations, fast and scalable algorithms, able to provide accurate responses in a short time are required. We consider the instantiation of variational inference and importance sampling, two well known tools for probabilistic inference, to the CLG case. The experimental results over synthetic networks show how a parallel version importance sampling, and more precisely evidence weighting, is a promising scheme, as it is accurate and scales up with respect to available computing resources.
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References
Attias, H.: A variational Bayesian framework for graphical models. In: Advances in neural information processing systems, pp. 209–215 (2000)
Beal, M.J.: Variational algorithms for approximate Bayesian inference. Ph.D. thesis, Gatsby Computational Neuroscience Unit, University College London (2003)
Cowell, R.G., Dawid, A.P., Lauritzen, S.L., Spiegelhalter, D.J.: Probabilistic Networks and Expert Systems. Statistics for Engineering and Information Science. Springer (1999). ISBN 0-387-98767-3
Fernández, A., Rumí, R., Salmerón, A.: Answering queries in hybrid Bayesian networks using importance sampling. Decis. Support Syst. 53, 580–590 (2012)
Fung, R., Chang, K.C.: Weighting and integrating evidence for stochastic simulation in Bayesian networks. In: Henrion, M., Shachter, R.D., Kanal, L.N., Lemmer, J.F. (eds.) Uncertainty in Artificial Intelligence, vol. 5, pp. 209–220. North-Holland, Amsterdam (1990)
Hammersley, J.M., Handscomb, D.C.: Monte Carlo Methods. Chapman & Hall, London (1964)
Jaakkola, T.S., Qi, Y.: Parameter expanded variational Bayesian methods. In: Advances in Neural Information Processing Systems, pp. 1097–1104 (2006)
Koller, D., Friedman, N.: Probabilistic Graphical Models: Principles and Techniques. MIT Press, Cambridge (2009)
Langseth, H., Nielsen, T.D., Rumí, R., Salmerón, A.: Mixtures of truncated basis functions. Int. J. Approximate Reasoning 53(2), 212–227 (2012)
Lauritzen, S.L., Jensen, F.: Stable local computation with conditional Gaussian distributions. Stat. Comput. 11(2), 191–203 (2001)
Lauritzen, S.L., Wermuth, N.: Graphical models for associations between variables, some of which are qualitative and some quantitative. Ann. Stat. 17, 31–57 (1989)
Moral, S., Rumí, R., Salmerón, A.: Mixtures of truncated exponentials in hybrid Bayesian networks. In: Benferhat, S., Besnard, P. (eds.) ECSQARU 2001. LNCS (LNAI), vol. 2143, p. 156. Springer, Heidelberg (2001)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers Inc., San Mateo (1988)
Rubinstein, R.Y.: Simulation and the Monte Carlo Method. Wiley, New York (1981)
Rumí, R., Salmerón, A.: Approximate probability propagation with mixtures of truncated exponentials. Int. J. Approximate Reasoning 45, 191–210 (2007)
Salmerón, A., Cano, A., Moral, S.: Importance sampling in Bayesian networks using probability trees. Comput. Stat. Data Anal. 34, 387–413 (2000)
Shachter, R.D., Kenley, C.: Gaussian influence diagrams. Manage. Sci. 35, 527–550 (1989)
Shenoy, P.P., West, J.C.: Inference in hybrid Bayesian networks using mixtures of polynomials. Int. J. Approximate Reasoning 52, 641–657 (2011)
Sun, W., Chang, K.C.: Probabilistic inference using linear Gaussian importance sampling for hybrid Bayesian networks. In: Signal Processing, Sensor Fusion, and Target Recognition XIV. Proceedings of SPIE, vol. 5809, pp. 322–329 (2005)
Winn, J.M., Bishop, C.M.: Variational message passing. J. Mach. Learn. Res. 6, 661–694 (2005)
Acknowledgments
This work was performed as part of the AMIDST project. AMIDST has received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no 619209.
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Salmerón, A. et al. (2015). Parallel Importance Sampling in Conditional Linear Gaussian Networks. In: Puerta, J., et al. Advances in Artificial Intelligence. CAEPIA 2015. Lecture Notes in Computer Science(), vol 9422. Springer, Cham. https://doi.org/10.1007/978-3-319-24598-0_4
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DOI: https://doi.org/10.1007/978-3-319-24598-0_4
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