{
  "annotation_id": "b3813036-ac56-463f-bd49-3c8308b5c5a5",
  "date_created": "2026-03-02T18:00:35.863000Z",
  "date_modified": "2026-03-02T18:00:35.863000Z",
  "file_hash": "dbe0e13c703be22bb392440c80a290c4a0ab865227b606ee9d84a24d03fc8062",
  "private": false,
  "record": {
    "abstract": "A complete solution for an inverse problem needs five main steps: choice of\nbasis functions for discretization, determination of the order of the model,\nestimation of the hyperparameters, estimation of the solution, and finally,\ncharacterization of the proposed solution. Many works have been done for the\nthree last steps. The first two have been neglected for a while, in part due to\nthe complexity of the problem. However, in many inverse problems, particularly\nwhen the number of data is very low, a good choice of the basis functions and a\ngood selection of the order become primary. In this paper, we first propose a\ncomplete solution within a Bayesian framework. Then, we apply the proposed\nmethod to an inverse elastic electron scattering problem.",
    "arxiv_id": "physics/0111020",
    "authors": [
      "A. Mohammad-Djafari"
    ],
    "categories": [
      "physics.data-an"
    ],
    "doi": "10.1063/1.1381850",
    "title": "Model selection for inverse problems: Best choice of basis functions and   model order selection",
    "url": "https://arxiv.org/abs/physics/0111020"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "8d3387c8-e815-47f9-8994-6b9e67a65635",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}