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.
Probabilistic Graphical Model for Continuous Variables
Intelligent Control & Learning Systems
Data-Driven Fault Detection and Reasoning for Industrial Monitoring ; Chapter : 14 ; 251-265
2022-01-03
15 pages
Article/Chapter (Book)
Electronic Resource
English
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