Assessing Search and Unsupervised Clustering Algorithms in Nested Sampling
Nested sampling is an efficient method for calculating Bayesian evidence in data analysis and partition functions of potential energies. It is based on an exploration using a dynamical set of sampling points that evolves to higher values of the sampled function. When several maxima are present, this...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/25/2/347 |
| _version_ | 1797621126325600256 |
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| author | Lune Maillard Fabio Finocchi Martino Trassinelli |
| author_facet | Lune Maillard Fabio Finocchi Martino Trassinelli |
| author_sort | Lune Maillard |
| collection | DOAJ |
| description | Nested sampling is an efficient method for calculating Bayesian evidence in data analysis and partition functions of potential energies. It is based on an exploration using a dynamical set of sampling points that evolves to higher values of the sampled function. When several maxima are present, this exploration can be a very difficult task. Different codes implement different strategies. Local maxima are generally treated separately, applying cluster recognition of the sampling points based on machine learning methods. We present here the development and implementation of different search and clustering methods on the nested_fit code. Slice sampling and the uniform search method are added in addition to the random walk already implemented. Three new cluster recognition methods are also developed. The efficiency of the different strategies, in terms of accuracy and number of likelihood calls, is compared considering a series of benchmark tests, including model comparison and a harmonic energy potential. Slice sampling proves to be the most stable and accurate search strategy. The different clustering methods present similar results but with very different computing time and scaling. Different choices of the stopping criterion of the algorithm, another critical issue of nested sampling, are also investigated with the harmonic energy potential. |
| first_indexed | 2024-03-11T08:52:19Z |
| format | Article |
| id | doaj.art-e7f9dd1e8e584503aa6563cd5606a0a0 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-11T08:52:19Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-e7f9dd1e8e584503aa6563cd5606a0a02023-11-16T20:24:15ZengMDPI AGEntropy1099-43002023-02-0125234710.3390/e25020347Assessing Search and Unsupervised Clustering Algorithms in Nested SamplingLune Maillard0Fabio Finocchi1Martino Trassinelli2Institut des Nanosciences de Paris, Sorbonne Université, CNRS, 75005 Paris, FranceInstitut des Nanosciences de Paris, Sorbonne Université, CNRS, 75005 Paris, FranceInstitut des Nanosciences de Paris, Sorbonne Université, CNRS, 75005 Paris, FranceNested sampling is an efficient method for calculating Bayesian evidence in data analysis and partition functions of potential energies. It is based on an exploration using a dynamical set of sampling points that evolves to higher values of the sampled function. When several maxima are present, this exploration can be a very difficult task. Different codes implement different strategies. Local maxima are generally treated separately, applying cluster recognition of the sampling points based on machine learning methods. We present here the development and implementation of different search and clustering methods on the nested_fit code. Slice sampling and the uniform search method are added in addition to the random walk already implemented. Three new cluster recognition methods are also developed. The efficiency of the different strategies, in terms of accuracy and number of likelihood calls, is compared considering a series of benchmark tests, including model comparison and a harmonic energy potential. Slice sampling proves to be the most stable and accurate search strategy. The different clustering methods present similar results but with very different computing time and scaling. Different choices of the stopping criterion of the algorithm, another critical issue of nested sampling, are also investigated with the harmonic energy potential.https://www.mdpi.com/1099-4300/25/2/347nested samplingslice samplingunsupervised clusteringharmonic potential |
| spellingShingle | Lune Maillard Fabio Finocchi Martino Trassinelli Assessing Search and Unsupervised Clustering Algorithms in Nested Sampling Entropy nested sampling slice sampling unsupervised clustering harmonic potential |
| title | Assessing Search and Unsupervised Clustering Algorithms in Nested Sampling |
| title_full | Assessing Search and Unsupervised Clustering Algorithms in Nested Sampling |
| title_fullStr | Assessing Search and Unsupervised Clustering Algorithms in Nested Sampling |
| title_full_unstemmed | Assessing Search and Unsupervised Clustering Algorithms in Nested Sampling |
| title_short | Assessing Search and Unsupervised Clustering Algorithms in Nested Sampling |
| title_sort | assessing search and unsupervised clustering algorithms in nested sampling |
| topic | nested sampling slice sampling unsupervised clustering harmonic potential |
| url | https://www.mdpi.com/1099-4300/25/2/347 |
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