{
  "id": "math-ph/0212005",
  "version": "v1",
  "published": "2002-12-02T15:22:25.000Z",
  "updated": "2002-12-02T15:22:25.000Z",
  "title": "Why Maximum Entropy? A Non-axiomatic Approach",
  "authors": [
    "M. Grendar, Jr.",
    "M. Grendar"
  ],
  "comment": "4 pages, MaxEnt 2001",
  "journal": "In: Bayesian inference and Maximum Entropy methods in Science and Engineering, R. L. Fry (ed.), AIP (Melville), 375-379, 2002",
  "categories": [
    "math-ph",
    "math.MP",
    "math.ST",
    "stat.TH"
  ],
  "abstract": "Ill-posed inverse problems of the form y = X p where y is J-dimensional vector of a data, p is m-dimensional probability vector which cannot be measured directly and matrix X of observable variables is a known J,m matrix, J < m, are frequently solved by Shannon's entropy maximization (MaxEnt). Several axiomatizations were proposed to justify the MaxEnt method (also) in this context. The main aim of the presented work is two-fold: 1) to view the concept of complementarity of MaxEnt and Maximum Likelihood (ML) tasks from a geometric perspective, and consequently 2) to provide an intuitive and non-axiomatic answer to the 'Why MaxEnt?' question.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2002-12-02T15:22:25.000Z"
    }
  ],
  "analyses": {
    "subjects": [
      "62B10",
      "94A17"
    ],
    "keywords": [
      "non-axiomatic approach",
      "maximum entropy",
      "shannons entropy maximization",
      "m-dimensional probability vector",
      "main aim"
    ],
    "tags": [
      "journal article"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 4,
      "language": "en",
      "license": "arXiv",
      "status": "editable",
      "adsabs": "2002math.ph..12005G"
    }
  }
}