Wichian Premchaiswadi

Siam University Thailand

Wichian Premchaiswadi received the M.Eng. and Ph.D. degrees in Electrical Engineering from Waseda University Japan. He is currently the Dean of the Graduate School of Information Technology in Business and Assistant President of Siam University, Thailand. He received the academic award for the best research conducted in 2008 from the Association of Private Higher Education Institution of Thailand. At present, he also works as the Secretary General of The Computer Association of Thailand under The Royal Patronage of His Majesty the King. His research interests include robust methods for autonomous computer vision, database systems, artificial intelligence, collaborative computing, human-computer interaction, decision support systems, group decision support systems, group decision making, parallel processing, image processing, nonparametric analysis, real-time vision systems, data mining, and pattern recognition.

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Latest work with IntechOpen by Wichian Premchaiswadi

Bayesian Belief Networks are a powerful tool for combining different knowledge sources with various degrees of uncertainty in a mathematically sound and computationally efficient way. A Bayesian network is a graphical model that encodes probabilistic relationships among variables of interest. When used in conjunction with statistical techniques, the graphical model has several advantages for data modeling. First, because the model encodes dependencies among all variables, it readily handles situations where some data entries are missing. Second, a Bayesian network can be used to learn causal relationships, and hence can be used to gain an understanding about a problem domain and to predict the consequences of intervention. Third, because the model has both causal and probabilistic semantics, it is an ideal representation for combining prior knowledge (which often comes in a causal form) and data. Fourth, Bayesian statistical methods in conjunction with Bayesian networks offer an efficient and principled approach to avoid the over fitting of data.

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