{
  "@context": "https://w3id.org/codemeta/3.0",
  "@type": "SoftwareSourceCode",
  "name": "Claim Verification: \u201cConsider a spike-train encoding model where spikes are generated by an inhomogeneous Poisson process with intensity lambda_t = f(eta_t), eta_t = x_t^T beta + h_t^T gamma + b, with convex parameter space for theta = (beta, gamma, b). If f is positive, convex, and log-concave, then the log-likelihood is concave in theta. Therefore every local maximum is global, ML fitting is a convex optimization problem, and the same holds for MAP inference under any log-concave prior on theta.\u201d \u2014 Proved",
  "description": "Verdict: PROVED",
  "version": "1.23.0",
  "dateCreated": "2026-04-18",
  "license": "https://spdx.org/licenses/MIT",
  "codeRepository": "https://github.com/yaniv-golan/proof-engine",
  "url": "https://proofengine.info/proofs/poisson-spike-train-loglik-concave/",
  "author": [
    {
      "@type": "Organization",
      "name": "Proof Engine"
    }
  ],
  "identifier": "https://doi.org/10.5281/zenodo.19645244"
}