Abstract
The problem of learning Bayesian network structure is well known to be NP–hard. It is therefore very important to develop efficient approximation techniques. We introduce an algorithm that within the framework of influence diagrams translates the structure learning problem into the strategy optimisation problem, for which we apply the Chen’s self–annealing stochastic optimisation algorithm. The effectiveness of our method has been tested on computer–generated examples.
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Matuszak, M., Miękisz, J. (2012). Stochastic Techniques in Influence Diagrams for Learning Bayesian Network Structure. In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds) Artificial Neural Networks and Machine Learning – ICANN 2012. ICANN 2012. Lecture Notes in Computer Science, vol 7552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33269-2_5
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DOI: https://doi.org/10.1007/978-3-642-33269-2_5
Publisher Name: Springer, Berlin, Heidelberg
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