{
  "id": "1908.05575",
  "version": "v1",
  "published": "2019-08-15T15:07:55.000Z",
  "updated": "2019-08-15T15:07:55.000Z",
  "title": "Mean-field limit and numerical analysis for Ensemble Kalman Inversion: linear setting",
  "authors": [
    "Zhiyan Ding",
    "Qin Li"
  ],
  "categories": [
    "math.NA",
    "cs.NA",
    "math.PR"
  ],
  "abstract": "Ensemble Kalman inversion (EKI) is a method introduced in [14] to find samples from the target posterior distribution in the Bayesian formulation. As a deviation from Ensemble Kalman filter [6], it introduces a pseudo-time along which the particles sampled from the prior distribution are pushed to fit the profile of the posterior distribution. To today, however, the thorough analysis on EKI is still unavailable. In this article, we analyze the continuous version of EKI, a coupled SDE system, and prove the solution to this SDE system convergences, as the number of particles goes to infinity, to the target posterior distribution in Wasserstein distance in finite time.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2019-08-15T15:07:55.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "ensemble kalman inversion",
      "mean-field limit",
      "numerical analysis",
      "target posterior distribution",
      "linear setting"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}