Mean Shift Cluster Recognition Method Implementation in the Nested Sampling Algorithm
Nested sampling is an efficient algorithm for the calculation of the Bayesian evidence and posterior parameter probability distributions. It is based on the step-by-step exploration of the parameter space by Monte Carlo sampling with a series of values sets called live points that evolve towards the...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-02-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/22/2/185 |
| _version_ | 1811263554325577728 |
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| author | Martino Trassinelli Pierre Ciccodicola |
| author_facet | Martino Trassinelli Pierre Ciccodicola |
| author_sort | Martino Trassinelli |
| collection | DOAJ |
| description | Nested sampling is an efficient algorithm for the calculation of the Bayesian evidence and posterior parameter probability distributions. It is based on the step-by-step exploration of the parameter space by Monte Carlo sampling with a series of values sets called live points that evolve towards the region of interest, i.e., where the likelihood function is maximal. In presence of several local likelihood maxima, the algorithm converges with difficulty. Some systematic errors can also be introduced by unexplored parameter volume regions. In order to avoid this, different methods are proposed in the literature for an efficient search of new live points, even in presence of local maxima. Here we present a new solution based on the mean shift cluster recognition method implemented in a random walk search algorithm. The clustering recognition is integrated within the Bayesian analysis program NestedFit. It is tested with the analysis of some difficult cases. Compared to the analysis results without cluster recognition, the computation time is considerably reduced. At the same time, the entire parameter space is efficiently explored, which translates into a smaller uncertainty of the extracted value of the Bayesian evidence. |
| first_indexed | 2024-04-12T19:47:22Z |
| format | Article |
| id | doaj.art-56d737e627ac4ae0918dcb181d50b16f |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-12T19:47:22Z |
| publishDate | 2020-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-56d737e627ac4ae0918dcb181d50b16f2022-12-22T03:18:55ZengMDPI AGEntropy1099-43002020-02-0122218510.3390/e22020185e22020185Mean Shift Cluster Recognition Method Implementation in the Nested Sampling AlgorithmMartino Trassinelli0Pierre Ciccodicola1Institut des NanoSciences de Paris, CNRS, Sorbonne Université, 4 Place Jussieu, 75005 Paris, FranceInstitut des NanoSciences de Paris, CNRS, Sorbonne Université, 4 Place Jussieu, 75005 Paris, FranceNested sampling is an efficient algorithm for the calculation of the Bayesian evidence and posterior parameter probability distributions. It is based on the step-by-step exploration of the parameter space by Monte Carlo sampling with a series of values sets called live points that evolve towards the region of interest, i.e., where the likelihood function is maximal. In presence of several local likelihood maxima, the algorithm converges with difficulty. Some systematic errors can also be introduced by unexplored parameter volume regions. In order to avoid this, different methods are proposed in the literature for an efficient search of new live points, even in presence of local maxima. Here we present a new solution based on the mean shift cluster recognition method implemented in a random walk search algorithm. The clustering recognition is integrated within the Bayesian analysis program NestedFit. It is tested with the analysis of some difficult cases. Compared to the analysis results without cluster recognition, the computation time is considerably reduced. At the same time, the entire parameter space is efficiently explored, which translates into a smaller uncertainty of the extracted value of the Bayesian evidence.https://www.mdpi.com/1099-4300/22/2/185nested samplingcluster analysismean shift methodbayesian evidencemodel comparison |
| spellingShingle | Martino Trassinelli Pierre Ciccodicola Mean Shift Cluster Recognition Method Implementation in the Nested Sampling Algorithm Entropy nested sampling cluster analysis mean shift method bayesian evidence model comparison |
| title | Mean Shift Cluster Recognition Method Implementation in the Nested Sampling Algorithm |
| title_full | Mean Shift Cluster Recognition Method Implementation in the Nested Sampling Algorithm |
| title_fullStr | Mean Shift Cluster Recognition Method Implementation in the Nested Sampling Algorithm |
| title_full_unstemmed | Mean Shift Cluster Recognition Method Implementation in the Nested Sampling Algorithm |
| title_short | Mean Shift Cluster Recognition Method Implementation in the Nested Sampling Algorithm |
| title_sort | mean shift cluster recognition method implementation in the nested sampling algorithm |
| topic | nested sampling cluster analysis mean shift method bayesian evidence model comparison |
| url | https://www.mdpi.com/1099-4300/22/2/185 |
| work_keys_str_mv | AT martinotrassinelli meanshiftclusterrecognitionmethodimplementationinthenestedsamplingalgorithm AT pierreciccodicola meanshiftclusterrecognitionmethodimplementationinthenestedsamplingalgorithm |