{
  "id": 14128825,
  "title": "Efficient approximate Bayesian inference for large, heterogeneous biological datasets",
  "url": "https://urgent.news/2026/10/12/efficient-approximate-bayesian-inference-for-large-heterogeneous",
  "topic": "science",
  "section": "Science",
  "published": "2026-10-12T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.10.05.756786v1?rss=1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Simulation-based inference (SBI) offers a route to connect biological data with mechanistic models. At the frontier of biology, datasets continue to grow in scale and richness, and increasingly record heterogeneity between individual observations that a mechanistic model can take into account. Approximate Bayesian Computation (ABC) remains a popular approach to SBI due to its ease of use, but…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}