Case Study
Bayesian Univariate Logistic Regression (Laplace + MH)
2025
- R
- Statistical Computing
- Bayesian Methods
- Parallel Computing
Posterior mode via Newton–Raphson; Laplace approximation; MH sampler; parallelized 60 fits with snow; posterior means + MLE sanity checks.
Problem & Motivation:
Approximate posteriors quickly and validate with sampling.
Data & Approach:
- Mode finding; Laplace evidence; MH initialized at mode; log-accept tracking; parallel runs.
Results:
- Laplace close to MH posterior means; acceptance stable with tuned proposals.
Limitations:
Univariate only; Gaussian priors.