| A Bayesian network is a graphical model for probabilistic relationships among a set of variables. This paper provides a natural tool for dealing with two problems, uncertainty and complexity, in applying mathematics and engineering. Over the last two decade, the Bayesian network has become a popular representation for encoding uncertain expert knowledge in expert systems. With the expansion of database scales, Bayesian networks have been applied in large-scales database for data mining and knowledge discovery and become a powerful tool for decision-making. Bayesian networks play an increasingly important role in the fields of knowledge Discovery and decision-making. This paper first presents a model for knowledge Discovery and decision-making based on Bayesian networks. And the paper discusses Bayesian networks theoretic in learning Bayesian network and application.This paper discusses the structure learning for Bayesian networks. The paper proposes a Maximum Mutual Information Metric with Restriction (MMI-R) based on Kullback-Leibler divergence, Mutual Information Metric, and Maximum Mutual Information Metric. This paper proposes using dimension and complexity of networks to be the combined restriction function. The function of MMI-R is composed of the restriction and Maximum Mutual Information. This paper proves that the function of MMI-R is the best choice in all structures and complexities. With the function of MMI-R, the problem of searching the best structure is turn to the optimization of MMI-R.This paper proposes an algorithm of Simulating Anneal (SA-MMI-R) to optimize the metric. The paper proposes three technologies to improve the algorithm in optimization. Firstly, contiguous states generating mechanisms is proposed. The mechanisms include three parts, exchanging, joining and deleting. Secondly, a new ending condition is proposed. Thirdly, a new variable is used tomemorize optimizations. Variational end is used to improve convergence speeds. Several experiments on standard data sets, such as Cancer, College, Asia and Alarm, are used to prove the advantages of SA-MMI-R proposed in the paper. The results indicated that the algorithm of Simulating Anneal with Restriction (SA-MMI-R) has more advantages than others.This paper discusses the parameter learning for Bayesian networks. The paper proposed a new algorithm to improve EM. Firstly, This algorithm divided the whole sample database into smaller blocks and deal with them respectively at E-Step. The algorithm composes their results outside the blocks. Secondly, an algorithm of Simulating Anneal is used to calculate Maximum. Several improvements are applied in algorithm, such as selection of initialization, neighborhood values and reiteration numbers. This paper also discusses the problem of Online Learning Parameter for Bayesian networks. Several experiments on standard data sets, such as Cancer, College, Asia and Alarm, are used to prove the advantages of improved EM proposed in the paper. The results indicated that the algorithm of improved EM has more advantages than standard EM.This paper discusses the inference and explanation of Bayesian networks. The paper discusses the basic metric of stochastic sampling algorithms and proposes a general stochastic sampling algorithm for Bayesian networks inference. This paper describes explanation of Bayesian networks from three ways, evidence explanation, inference explanation and explanation. This paper gives a detailed explanation of probability relationship.This paper applies the Bayesian networks in Flood Decision Supporting System. Three models based on Bayesian networks are proposed. Rainfall forecast model is presented for accordant rainfall junction conflux. Flood forecast model is presented for forecasting flood level and flux. Flood risk model is presented for analyzing risk and possible economic losing. The entire models are used for Flood Decision Supporting. In the end, the paper gives a particular explanation of Flood risk model for knowledge Discovery and decision-making...
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