{
  "@context": "https://schema.org",
  "@type": "article",
  "@id": "https://anchorfact.org/kb/bayesian-deep-learning",
  "headline": "Bayesian Deep Learning: Uncertainty Quantification and Robust Predictions",
  "description": "Bayesian Deep Learning equips neural networks with uncertainty estimates — knowing when the model is likely to be wrong. From Monte Carlo Dropout to Deep Ensembles and modern Bayesian approximations, UQ is critical for safety-critical AI (medical, autonomous driving, finance).",
  "dateCreated": "2026-05-24T02:49:13.584Z",
  "dateModified": "2026-05-24",
  "author": {
    "@type": "Organization",
    "name": "AnchorFact"
  },
  "publisher": {
    "@type": "Organization",
    "name": "AnchorFact",
    "url": "https://anchorfact.org"
  },
  "license": "https://creativecommons.org/licenses/by/4.0/",
  "anchorfact:confidence": "high",
  "anchorfact:generationMethod": "ai_assisted",
  "citation": [
    {
      "@type": "CreativeWork",
      "name": "Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift",
      "sameAs": "https://arxiv.org/abs/1906.02530"
    },
    {
      "@type": "CreativeWork",
      "name": "Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles",
      "sameAs": "https://arxiv.org/abs/1612.01474"
    }
  ]
}