Bayesian Inference: Overview

3-Second Blueprint (English)

1. Computes full posterior distributions P(theta|x) instead of MLE point estimates, enabling uncertainty propagation in dynamical forecasts.
2. Iteratively updates uniform priors using Beta-Binomial conjugacy so each posterior feeds directly into the next sequential data step.
3. Falls back to Kernel Density Estimation and Monte Carlo sampling when non-conjugate likelihoods prevent closed-form posterior updates.