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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
PubMed
Summary
This summary is machine-generated.

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.

Keywords:
harmonic potentialnested samplingslice samplingunsupervised clustering

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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.