Big Data Analysis with Bayesian Network

Big Data concern large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data are now rapidly expanding in all science and engineering domains, including physical, biological and biomedical sciences Over the past few decades, with the development of automatic identification, data capture and storage technologies, people generate data much faster and collect data much bigger than ever before in business, science, engineering, education and other areas. Big data has emerged as an important area of study for both practitioners and researchers. Bayesian network is the main research method in the field of artificial intelligence for uncertainty problem representation and processing of and health grading evaluation is one of the important technology in health management. Through the analysis of different models and study methods of Bayesian theory, combining the characteristics of the three-state dividing of system, the three states of dynamic Bayesian network health evaluation method is put forward, which calculates the dynamic Bayesian network using the hidden Markov model. Then EM algorithm and CRLA algorithm for dynamic Bayesian networks parameter learning are studied. Finally, based on Pspice simulation software and the DBN software toolbox of Matlab, the three-stage amplifier circuit Bayesian evaluation model of the three health states is built, and the corresponding application instructions and results are obtained.