The Bayesian Estimation Method In Big Data Era

With the rapid development of information and science and techno?logy,the generation of massive data,traditional manual processing and statistical methods of analyzing data cannot meet the needs of reality.Especially in the era of big data,the number of data is huge and the structure is complex,and the traditional processing method can not be effective in these data.In today,s era of big data,with the data and process the data effectively,for an enterprise,govermment,and even the whole nation is vital,so more attention paid to the study of processing huge amounts of data.After decades of research,big data analysis has developed into the most important data processing and analysis theory,and has been widely used.This thesis mainly introduces the methods of bayesian estimation in the context of the development of big data.The main contents are as follows:the origin of big data,the characteristic of big data,and the type of big data,and then introduces the history of development,data mining and its function,then expounds the characteristics of data mining and data mining and machine learning,statistics,data warehouse and the relationship between the intelligent decision making in areas such as;The concept and application of bayesian classification and naive bayes classification;Definition and theorem of decision theory and statistical decision theory;Related concepts and theories of linear discriminant function and quadratic discriminant function;The basic concept of bayesian network,the construction of bayesian network,and K2 mountain climbing algorithm and SEM algorithm,and improved the K2 climbing algorithm,and put forward their own ideas.