{"paper":{"title":"Strategic Monte Carlo Methods for State and Parameter Estimation in High Dimensional Nonlinear Problems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.data-an"],"primary_cat":"stat.ME","authors_text":"Henry D. I. Abarbanel, Sasha Shirman","submitted_at":"2018-05-24T18:08:58Z","abstract_excerpt":"In statistical data assimilation one seeks the largest maximum of the conditional probability distribution $P(\\mathbf{X},\\mathbf{p}|\\mathbf{Y})$ of model states, $\\mathbf{X}$, and parameters,$\\mathbf{p}$, conditioned on observations $\\mathbf{Y}$ through minimizing the `action', $A(\\mathbf{X}) = -\\log P(\\mathbf{X},\\mathbf{p}|\\mathbf{Y})$. This determines the dominant contribution to the expected values of functions of $\\mathbf{X}$ but does not give information about the structure of $P(\\mathbf{X},\\mathbf{p}|\\mathbf{Y})$ away from the maximum. We introduce a Monte Carlo sampling method, called S"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.09838","kind":"arxiv","version":1},"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"}