{
  "id": "1509.02900",
  "version": "v1",
  "published": "2015-09-09T19:33:31.000Z",
  "updated": "2015-09-09T19:33:31.000Z",
  "title": "Statistical Inference, Learning and Models in Big Data",
  "authors": [
    "Beate Franke",
    "Jean-François Plante",
    "Ribana Roscher",
    "Annie Lee",
    "Cathal Smyth",
    "Armin Hatefi",
    "Fuqi Chen",
    "Einat Gil",
    "Alex Schwing",
    "Alessandro Selvitella",
    "Michael M. Hoffman",
    "Roger Grosse",
    "Dieter Hendricks",
    "Nancy Reid"
  ],
  "comment": "Thematic Program on Statistical Inference, Learning, and Models for Big Data, Fields Institute",
  "categories": [
    "stat.ML",
    "cs.LG"
  ],
  "abstract": "Big data provides big opportunities for statistical inference, but perhaps even bigger challenges, often related to differences in volume, variety, velocity, and veracity of information when compared to smaller carefully collected datasets. From January to June, 2015, the Canadian Institute of Statistical Sciences organized a thematic program on Statistical Inference, Learning and Models in Big Data. This paper arose from presentations and discussions that took place during the thematic program.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2015-09-09T19:33:31.000Z"
    }
  ],
  "analyses": {
    "subjects": [
      "62-07",
      "I.2.6",
      "I.2.3",
      "I.5.1",
      "G.3"
    ],
    "keywords": [
      "big data",
      "statistical inference",
      "thematic program",
      "big opportunities",
      "canadian institute"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable",
      "adsabs": "2015arXiv150902900F"
    }
  }
}