{
  "id": "2001.11760",
  "version": "v1",
  "published": "2020-01-31T10:46:30.000Z",
  "updated": "2020-01-31T10:46:30.000Z",
  "title": "Convolutional Neural Networks as Summary Statistics for Approximate Bayesian Computation",
  "authors": [
    "Mattias Åkesson",
    "Prashant Singh",
    "Fredrik Wrede",
    "Andreas Hellander"
  ],
  "categories": [
    "stat.ML",
    "cs.LG"
  ],
  "abstract": "Approximate Bayesian Computation is widely used in systems biology for inferring parameters in stochastic gene regulatory network models. Its performance hinges critically on the ability to summarize high-dimensional system responses such as time series into a few informative, low-dimensional summary statistics. The quality of those statistics critically affect the accuracy of the inference. Existing methods to select the best subset out of a pool of candidate statistics do not scale well with large pools. Since it is imperative for good performance this becomes a serious bottleneck when doing inference on complex and high-dimensional problems. This paper proposes a convolutional neural network architecture for automatically learning informative summary statistics of temporal responses. We show that the proposed network can effectively circumvent the statistics selection problem as a preprocessing step to ABC for a challenging inference problem learning parameters in a high-dimensional stochastic genetic oscillator. We also study the impact of experimental design on network performance by comparing different data richness and different data acquisition strategies.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2020-01-31T10:46:30.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "approximate bayesian computation",
      "convolutional neural network",
      "inference problem learning parameters",
      "learning informative summary statistics",
      "stochastic gene regulatory network models"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}