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Bayesian networks (BNs) are modern tools for modeling phenomena in dynamic and static systems and are used in different subjects such as disease diagnosis, weather forecasting, decision making and clustering. A BN is a graphical-probabilistic model which represents causal relations among random variables and consists of a directed acyclic graph and a set of conditional probabilities. Structure learning and parameter learning are two main subjects in BNs. In this paper, we consider a BN with a known structure and then, by simulate some data, we try to learn structure of the network using two well-known algorithms, namely, PC and $ K_{2} $ algorithms. Then, we learn parameters of the network and derive the maximum likelihood, maximum a posteriori and posterior mean estimates of the corresponding parameters. Furthermore, we compare performance of the estimates using the Kullback-Leibler divergence criteria and finally, utilizing a real data set, we consider the structure and parameter learning tasks to illustrate practical utility of the proposed methods.

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‎A Bayesian network is a graphical model that represents a set of random variables and their causal relationship via a Directed Acyclic Graph (DAG)‎. ‎There are basically two methods used for learning Bayesian network‎: ‎parameter-learning and structure-learning‎. ‎One of the most effective structure-learning methods is K2 algorithm‎. ‎Because the performance of the K2 algorithm depends on node ordering‎, ‎more effective node ordering inference methods are needed‎. ‎In this paper‎, ‎based on the fact that the parent and child variables are identified by estimated Markov Blanket (MB)‎, ‎we first estimate the MB of a variable using Grow-Shrink algorithm‎, ‎then determine the candidate parents of a variable by evaluating the conditional frequencies using Dirichlet probability density function‎. ‎Then the candidate parents are used as input for the K2 algorithm‎. ‎Experimental results for most of the datasets indicate that our proposed method significantly outperforms previous method‎.