{
  "id": "1902.03394",
  "version": "v1",
  "published": "2019-02-09T09:22:24.000Z",
  "updated": "2019-02-09T09:22:24.000Z",
  "title": "A stochastic version of Stein Variational Gradient Descent for efficient sampling",
  "authors": [
    "Lei Li",
    "Jian-Guo Liu",
    "Zibu Liu",
    "Jianfeng Lu"
  ],
  "categories": [
    "stat.ML",
    "cs.LG",
    "math.PR"
  ],
  "abstract": "We propose in this work RBM-SVGD, a stochastic version of Stein Variational Gradient Descent (SVGD) method for efficiently sampling from a given probability measure and thus useful for Bayesian inference. The method is to apply the Random Batch Method (RBM) for interacting particle systems proposed by Jin et al to the interacting particle systems in SVGD. While keeping the behaviors of SVGD, it reduces the computational cost, especially when the interacting kernel has long range. Numerical examples verify the efficiency of this new version of SVGD.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2019-02-09T09:22:24.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "stein variational gradient descent",
      "stochastic version",
      "efficient sampling",
      "interacting particle systems",
      "random batch method"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}