{"paper":{"title":"Cosmological parameter estimation using Particle Swarm Optimization (PSO)","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.IM","gr-qc"],"primary_cat":"astro-ph.CO","authors_text":"Astrophysics (IUCAA), Jayanti Prasad, Pune (India)), Tarun Souradeep (Inter-University Centre for Astronomy","submitted_at":"2011-08-29T15:23:20Z","abstract_excerpt":"Obtaining the set of cosmological parameters consistent with observational data is an important exercise in current cosmological research. It involves finding the global maximum of the likelihood function in the multi-dimensional parameter space. Currently sampling based methods, which are in general stochastic in nature, like Markov-Chain Monte Carlo(MCMC), are being commonly used for parameter estimation. The beauty of stochastic methods is that the computational cost grows, at the most, linearly in place of exponentially (as in grid based approaches) with the dimensionality of the search sp"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1108.5600","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}