Assessing Search and Unsupervised Clustering Algorithms in Nested Sampling
Lune Maillard1, Fabio Finocchi1, Martino Trassinelli1
1Institut des Nanosciences de Paris, Sorbonne Université, CNRS, 75005 Paris, France.
Entropy (Basel, Switzerland)
|February 25, 2023
Summary
Nested sampling, a Bayesian evidence calculation method, struggles with multiple data peaks. New search and clustering strategies in nested_fit code improve accuracy and efficiency, with slice sampling being the most stable.
Area of Science:
- Computational Physics
- Statistical Mechanics
- Machine Learning
Background:
- Nested sampling is crucial for Bayesian evidence and partition function calculations.
- Exploring complex potential energy landscapes with multiple maxima presents a significant challenge.
- Existing methods often rely on machine learning for cluster recognition of sampling points.
Purpose of the Study:
- To develop and implement novel search and clustering strategies for the nested_fit code.
- To enhance the efficiency and accuracy of nested sampling in complex scenarios.
- To compare the performance of different search and clustering algorithms.
Main Methods:
- Implemented slice sampling and uniform search methods alongside the existing random walk.
- Developed three new cluster recognition methods for sampling points.
- Conducted benchmark tests including model comparison and harmonic energy potential analysis.
Main Results:
- Slice sampling demonstrated superior stability and accuracy compared to other search strategies.
- New clustering methods yielded comparable results but varied significantly in computational cost and scalability.
- Investigated the impact of different stopping criteria on algorithm performance.
Conclusions:
- Slice sampling is the most robust search strategy for nested sampling.
- The choice of clustering method impacts computational efficiency more than accuracy.
- Optimizing search and clustering strategies is key to improving nested sampling performance.
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