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Stochastic Processes

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Stochastic Processes

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Research Frontiers in Markov Chain Monte Carlo Methods

Development of computational algorithms using Markov chains for Bayesian inference and sampling from complex distributions.

Adaptive Tempering in High-Dimensional Posterior Landscapes

Convergence Diagnostics Beyond Gelman-Rubin Statistics

Rare Event Sampling in Intractable Likelihood Models

Geometric Mixing and Manifold-Aware MCMC

Scalable Inference for Billion-Parameter Systems

Reversible Jump Dynamics in Transdimensional Spaces

Stein Variational Gradient Flows and MCMC Hybrids

Multilevel MCMC for Hierarchical Uncertainty Quantification

Kinetic Energy Tuning in Hamiltonian Samplers

Delayed Acceptance Screening in Expensive Likelihood Evaluations

All Stochastic Processes PhD categories