{
  "id": "1805.01318",
  "version": "v1",
  "published": "2018-05-03T14:17:10.000Z",
  "updated": "2018-05-03T14:17:10.000Z",
  "title": "Stochastic duality and eigenfunctions",
  "authors": [
    "Frank Redig",
    "Federico Sau"
  ],
  "categories": [
    "math.PR"
  ],
  "abstract": "We start from the observation that, anytime two Markov generators share an eigenvalue, the function constructed from the product of the two eigenfunctions associated to this common eigenvalue is a duality function. We push further this observation and provide a full characterization of duality relations in terms of spectral decompositions of the generators for finite state space Markov processes. Moreover, we study and revisit some well-known instances of duality, such as Siegmund duality, and extract spectral information from it. Next, we use the same formalism to construct all duality functions for some solvable examples, i.e., processes for which the eigenfunctions of the generator are explicitly known.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2018-05-03T14:17:10.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "stochastic duality",
      "eigenfunctions",
      "finite state space markov processes",
      "duality function",
      "extract spectral information"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}