Factor Markov Chain Research Based On Factor Analysis Theory

In order to meet the requirements of artificial intelligence for dealing with randomness and uncertainty of background relations,based on the background distribution of factor space and fuzzy background relationship theory,the measurability of factor space is studied,and measurable factors,measurable factors,and factors are obtained.Probability distribution function,In addition,the measurability,measurability and measurability of factor space are explained,and the theory of factor analysis and information gain are applied to the analysis of hypertension risk factors in the new rural population in Liaoning province.Based on the theory of factor analysis,random process,Markov process and decision Markov process are defined,and Markov process and properties are studied.The case study shows that using factor space as the basic space provides a prediction method for dealing with the random process of factors without latency,which is fundamental to the theory of factor space,and provides a theoretical basis for the decision-making process of dealing with artificial intelligence.The factor Markov chain can not only solve the decision-making problem of no latency,but also explain the reason of the probability of state transition based on factor analysis theory.When the factors that determine the state are independent of each other,the calculation method of the state transition probability is given.Then randomness is introduced into the factor space to discuss the reasons for the generation of transfer matrices in the Markov process.The theory,nature and application of factor random process,factor Markov process are given.The study shows that using factor space as the basic space provides a new prediction method for dealing with factor random process with no latency,which is fundamental to the decision-making process of artificial intelligence.