Bayesian network is one of the main tools for data mining.In such cases as large equipment fault diagnosis

geological disaster forecast

operational decision

etc

good results are expected to achieve based on small data sets.Therefore

this article focuses on the problem of learning Bayesian network from small data sets.Firstly

the structure constraint model based on the probability distribution of the connection was built.Then

the improved-Bayesian Dirichlet-binary particle swarm optimization algorithm was proposed.Secondly

the monotonicity parameter constraint model was defined and the monotonicity constraint estimation algorithm was proposed.Finally

the proposed algorithm was applied to construct the threat assessment model.Then

the model was used for reasoning with the variable elimination method.Experimental results reveal that the structure learning algorithm outperforms classical binary particle swarm optimization algorithm and the parameter learning method surpasses maximum likelihood estimation

isotonic regression and convex optimization method for small data sets.The threat assessment model is also proved to be effective.