Probability density estimation via an infinite Gaussian mixture model: application to statistical process monitoring
By Tao Chen, Julian Morris, Elaine B. Martin. 2006
97 citations recorded by the source corpus.
Subfields: Control and Systems Engineering, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/07d26d04a59f5049dfc190edbb553540b9f33fd3
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Control and Systems Engineering
- Zhiqiang Ge
- Biao Huang
- Steven X. Ding
- Thaddeus Neururer
- Fei Ye
- Ben Van Vliet
- Adrian G. Bors
- Andrew Kumiega
In Statistics and Probability
- Thaddeus Neururer
- Biao Huang
- Steven X. Ding
- Fei Ye
- Zhiqiang Ge
- Ben Van Vliet
- Adrian G. Bors
- Andrew Kumiega
Citations in the corpus
- Lifelong Infinite Mixture Model Based on Knowledge-Driven Dirichlet Process (2021, background): “πK} can be indirectly realized by the inference of the indicator variable, [5] :”
- Data Mining and Analytics in the Process Industry: The Role of Machine Learning (2017, background): “how many clusters are among the whole dataset, clustering analysis can be carried out [29].”
- Trading system capability (2014, methodology): “(The more advanced methods of calculating SPC and capability for non-normal processes (see, for example, Thissen et al. (2005) or Chen et al. (2006)) are not discussed in this paper to ensure the reader can follow the pedagogical example in this paper.)”