Most of the sampled data in complex industrial processes are sequential in time. Therefore, the traditional BN learning mechanisms have limitations on the value of probability and cannot be applied to the time series. The model established in Chap. 13 is a graphical model similar to a Bayesian network, but its parameter learning method can only handle the discrete variables. This chapter aims at the probabilistic graphical model directly for the continuous process variables, which avoids the assumption of discrete or Gaussian distributions.


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    Title :

    Probabilistic Graphical Model for Continuous Variables


    Additional title:

    Intelligent Control & Learning Systems


    Contributors:
    Wang, Jing (author) / Zhou, Jinglin (author) / Chen, Xiaolu (author)


    Publication date :

    2022-01-03


    Size :

    15 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English





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