18488984. COUNT-TYPE QUALITY VARIABLE PREDICTION METHOD BASED ON VARIATIONAL BAYESIAN GAUSSIAN-POISSON MIXED REGRESSION MODEL simplified abstract (ZHEJIANG UNIVERSITY)
COUNT-TYPE QUALITY VARIABLE PREDICTION METHOD BASED ON VARIATIONAL BAYESIAN GAUSSIAN-POISSON MIXED REGRESSION MODEL
Organization Name
Inventor(s)
Jinchuan Qian of Hangzhou (CN)
COUNT-TYPE QUALITY VARIABLE PREDICTION METHOD BASED ON VARIATIONAL BAYESIAN GAUSSIAN-POISSON MIXED REGRESSION MODEL - A simplified explanation of the abstract
This abstract first appeared for US patent application 18488984 titled 'COUNT-TYPE QUALITY VARIABLE PREDICTION METHOD BASED ON VARIATIONAL BAYESIAN GAUSSIAN-POISSON MIXED REGRESSION MODEL
Simplified Explanation:
This patent application describes a method for predicting count-type quality variables using a variational Bayesian Gaussian-Poisson mixed regression model. The method is designed for analyzing and predicting data where the dependent variable is count data and the independent variables are continuous values. By utilizing Gaussian mixed distribution and Poisson mixed regression distribution, the model can effectively handle both types of data and provide discrete probability estimation for count data.
Key Features and Innovation:
- Prediction method for count-type quality variables
- Variational Bayesian Gaussian-Poisson mixed regression model
- Utilizes Gaussian mixed distribution and Poisson mixed regression distribution
- Shares the same mixed coefficient for both distributions
- Parameter learning through variational inference technology
Potential Applications:
- Industrial process monitoring and control
- Quality control in manufacturing
- Predictive maintenance in machinery
- Financial forecasting
Problems Solved:
- Traditional soft-sensing methods lack discrete probability estimation for count data
- Multiple modals in process variables and quality variables due to various working conditions
Benefits:
- Accurate prediction of count-type quality variables
- Improved data analysis for mixed data types
- Enhanced decision-making in industrial processes
Commercial Applications: Predictive Analytics for Industrial Quality Control: Enhancing Manufacturing Efficiency and Product Quality
Prior Art: Readers interested in prior art related to this technology may explore research on variational Bayesian methods in regression modeling and mixed distribution models in data analysis.
Frequently Updated Research: Stay updated on the latest advancements in variational Bayesian methods and mixed regression models for count data prediction.
Questions about Count-Type Quality Variable Prediction: 1. How does the variational Bayesian Gaussian-Poisson mixed regression model improve prediction accuracy compared to traditional methods? 2. What are the potential challenges in implementing this method in real-world industrial applications?
Original Abstract Submitted
A count-type quality variable prediction method based on a variational Bayesian Gaussian-Poisson mixed regression model. This method can be used for data analysis and prediction when the dependent variable is count data and the independent variables are continuous values. Its core is to use Gaussian mixed distribution and Poisson mixed regression distribution to fit the continuous data and count data respectively, assume that the two mixed distributions share the same mixed coefficient, and adopt variational inference technology for parameter learning of the model. The present disclosure overcomes the limitation that the traditional soft-sensing method cannot provide discrete probability estimation for count data, and can solve the problem that process variables and quality variable present multiple modals due to multiple working conditions in the industrial process.
