{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T16:22:57Z","timestamp":1790785377119,"version":"4.1.0"},"reference-count":58,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2020,2,6]],"date-time":"2020-02-06T00:00:00Z","timestamp":1580947200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/e22020185","type":"journal-article","created":{"date-parts":[[2020,2,7]],"date-time":"2020-02-07T03:13:27Z","timestamp":1581045207000},"page":"185","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Mean Shift Cluster Recognition Method Implementation in the Nested Sampling Algorithm"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4414-1801","authenticated-orcid":false,"given":"Martino","family":"Trassinelli","sequence":"first","affiliation":[{"name":"Institut des NanoSciences de Paris, CNRS, Sorbonne Universit\u00e9, 4 Place Jussieu, 75005 Paris, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pierre","family":"Ciccodicola","sequence":"additional","affiliation":[{"name":"Institut des NanoSciences de Paris, CNRS, Sorbonne Universit\u00e9, 4 Place Jussieu, 75005 Paris, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"103511","DOI":"10.1103\/PhysRevD.66.103511","article-title":"Cosmological parameters from CMB and other data: A Monte Carlo approach","volume":"66","author":"Lewis","year":"2002","journal-title":"Phys. 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