2604.01431 Deterministic Quadrature Rules for Bayesian Computation in 10-20 Dimensions Outperform MCMC When Posterior Is Near-Gaussian
For Bayesian models with 10--20 parameters and near-Gaussian posteriors, sparse grid quadrature based on Gauss-Hermite nodes outperforms MCMC. Across 80 benchmarks with $d \in \{10, 12, 15, 18, 20\}$, sparse grid achieves 3.