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Streamlined variational inference for higher level group-specific curve models
M Menictas1, T H Nolan1,2, D G Simpson3
1School of Mathematical and Physical Sciences, University of Technology Sydney, Australia.
Statistical Modelling
|January 10, 2022
Summary
This study introduces streamlined variational inference for complex group-specific curve models. The methods advance analysis of nested data, particularly in ultrasound technology applications.
Area of Science:
- Statistics
- Machine Learning
Background:
- Group-specific curve models analyze individual smooth functions within groups.
- Extending these models to multiple nested levels presents computational challenges.
Purpose of the Study:
- To develop streamlined variational inference methods for higher-level group-specific curve models.
- To address the computational complexity of analyzing nested data structures.
Main Methods:
- Systematic development of variational inference for two-level and three-level group-specific curve models.
- Leveraging sparse matrix infrastructure for computational efficiency.
Main Results:
- Demonstrated a viable approach for streamlined variational inference in hierarchical models.
- The methods are motivated by and applicable to ultrasound data analysis.
Conclusions:
- The systematic approach provides a foundation for higher-level group-specific curve models.
- The developed methods enhance the analysis of complex, nested data structures.
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