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Computational Statistics

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Computational Statistics

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Research Frontiers in Markov Chain Monte Carlo Convergence Analysis

Theoretical and empirical study of mixing times, convergence diagnostics, and efficiency measures for MCMC sampling algorithms.

Spectral Geometry of High-Dimensional Markov Chains
Convergence Diagnostics Beyond Potential Scale Reduction
Mixing Time Acceleration in Constrained Spaces
Gradient Information in MCMC Trajectory Analysis
Phase Transitions in Sampler Escape Dynamics
Information-Theoretic Bounds on Burn-In Duration
Adaptive Tempering and Energy Landscape Navigation
Coupling Methods for Non-Reversible Markov Flows
Wasserstein Geometry in Convergence Rate Estimation
Rare Event Detection in Ill-Mixing Regimes

All Computational Statistics PhD categories