Augmenting learning function to Bayesian network inferences with maximum likelihood parameters |
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| Authors: | WeiYi Liu Kun Yue JiaDong Zhang |
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| Affiliation: | 1. Case Western Reserve University, Center for Health Care Research & Policy, MetroHealth Medical Center, Cleveland, OH, USA;2. Case Western Reserve University, Department of Epidemiology and Biostatistics, Cleveland, OH, USA;3. Mellen Center for Multiple Sclerosis Treatment and Research, Cleveland Clinic, Cleveland, OH, USA;1. Physical Oceanography Laboratory, Ocean University of China, Qingdao 266100, China;2. State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, State Oceanic Administration, Hangzhou 310012, China;3. HSE Department, Shenzhen Branch of China National Offshore Oil Corporation Ltd, Shenzhen 518067, China;1. University of Regina, Department of Computer Science, Regina, S4S 0A2, Canada;2. Aalborg University, Department of Computer Science, Aalborg, DK-9000, Denmark;3. HUGIN EXPERT A/S, Aalborg, DK-9000, Denmark;1. University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, Ivana Lu?i?a 5, Zagreb, Croatia;2. Wikki Ltd, 459 Southbank House, SE1 7SJ London, United Kingdom;3. Bureau Veritas, 67/71, 92200 boulevard du Château, Neuilly-sur-Seine, France;1. School of Environmental Sciences, University of Liverpool, Liverpool L69 7ZT, UK;2. School of Engineering, University of Liverpool, Liverpool L69 3GQ, UK;3. School of Environmental Sciences, University of Hull, Cottingham Road, Hull HU6 7RX, UK;4. National Oceanography Centre, Joseph Proudman Building, 6 Brownlow Street, Liverpool L3 5DA, UK;5. FTZ-Westkste/Coastal Research Laboratory, Christian-Albrechts-Universitt zu Kiel, Kiel, Germany;6. Institute for Risk and Uncertainty, University of Liverpool, Liverpool, L69 3GH, UK |
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| Abstract: | Computing the posterior probability distribution for a set of query variables by search result is an important task of inferences with a Bayesian network. Starting from real applications, it is also necessary to make inferences when the evidence is not contained in training data. In this paper, we are to augment the learning function to Bayesian network inferences, and extend the classical “search”-based inferences to “search + learning”-based inferences. Based on the support vector machine, we use a class of hyperplanes to construct the hypothesis space. Then we use the method of solving an optimal hyperplane to find a maximum likelihood hypothesis for the value not contained in training data. Further, we give a convergent Gibbs sampling algorithm for approximate probabilistic inference with the presence of maximum likelihood parameters. Preliminary experiments show the feasibility of our proposed methods. |
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