因素空间理论的因素Markov过程

Factor Markov process of factor space theory

为适应大数据的随机性,在因素空间中的背景分布基础上,研究了因素空间的可测性,得到可测因素、可测因素空间;进而将随机性引入因素空间,给出因素随机过程、因素Markov过程理论、性质及应用.融合了Markov过程的随机性和因素空间的数据推理,拓展了因素空间理论的随机性领域,又从另一个角度解释了背景分布、随机过程、Markov过程产生的原因为处理具有无后效性的因素随机过程提供新的预测方法.

In order to adapt to the randomness of large data,based on the background distribution in the factor space,the measurability of the factor space is studied,and the measurability and measurability factors space are obtained.Then the randomness is introduced into the factor space,and the factor random process,factor Markov process theory,nature,and application are given.The data reasoning of Markov process randomness and factor space is combined,and the randomness field of factor space theory is expanded.From another point of view,the reasons for background distribution,random process,and Markov process are explained.It provides a new prediction method for dealing with factors with no latency.