{
  "name": "Optimiser families and algorithms named in the PyBOP paper",
  "description": "Compiled from the PyBOP JOSS paper and arXiv preprint (v1): the optimiser or sampler families PyBOP supports, the algorithms named in each, and whether each returns a point estimate or posterior samples.",
  "page": "https://casrai.org/guides/pybop",
  "publisher": "CASRAI",
  "license": "https://creativecommons.org/licenses/by/4.0/",
  "dateModified": "2026-11-14T12:38:29Z",
  "temporalCoverage": "2025",
  "spatialCoverage": null,
  "columns": [
    {
      "name": "Family",
      "unit": "",
      "description": ""
    },
    {
      "name": "Algorithms named in the paper",
      "unit": "",
      "description": ""
    },
    {
      "name": "Output type",
      "unit": "",
      "description": ""
    }
  ],
  "rows": [
    {
      "Family": "Gradient-based",
      "Algorithms named in the paper": "AdamW, iRProp-, gradient descent, SciPy minimize",
      "Output type": "Point estimate"
    },
    {
      "Family": "Evolutionary",
      "Algorithms named in the paper": "CMA-ES, xNES, sNES, differential evolution",
      "Output type": "Point estimate"
    },
    {
      "Family": "(Meta)heuristic",
      "Algorithms named in the paper": "Particle swarm, Nelder-Mead, cuckoo search",
      "Output type": "Point estimate"
    },
    {
      "Family": "Bayesian sampling",
      "Algorithms named in the paper": "NUTS, Hamiltonian Monte Carlo, Haario-Bardenet, emcee hammer (mostly via PINTS)",
      "Output type": "Posterior samples"
    }
  ],
  "sources": [
    {
      "title": "PyBOP preprint, arXiv:2412.15859v1",
      "url": "https://arxiv.org/html/2412.15859v1",
      "accessed": "2026-11-14"
    },
    {
      "title": "PyBOP, Journal of Open Source Software 10(116), 7874",
      "url": "https://joss.theoj.org/papers/10.21105/joss.07874",
      "accessed": "2026-11-14"
    }
  ]
}