{
  "id": "1702.07894",
  "version": "v1",
  "published": "2017-02-25T13:59:08.000Z",
  "updated": "2017-02-25T13:59:08.000Z",
  "title": "Convergence Analysis of the Ensemble Kalman Filter for Inverse Problems: the Noisy Case",
  "authors": [
    "Claudia Schillings",
    "Andrew Stuart"
  ],
  "categories": [
    "math.NA"
  ],
  "abstract": "We present an analysis of the ensemble Kalman filter for inverse problems based on the continuous time limit of the algorithm. The analysis of the dynamical behaviour of the ensemble allows to establish well-posedness and convergence results for a fixed ensemble size. We will build on the results presented in [Schillings, Stuart 2017] and generalise them to the case of noisy observational data, in particular the influence of the noise on the convergence will be investigated, both theoretically and numerically.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2017-02-25T13:59:08.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "ensemble kalman filter",
      "inverse problems",
      "noisy case",
      "convergence analysis",
      "noisy observational data"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}