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Computational Statistics

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Computational Statistics

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Research Frontiers in Probabilistic Graphical Models Inference

Computational methods for exact and approximate inference in Bayesian networks, Markov random fields, and factor graphs.

Approximate Inference in High-Dimensional Discrete Spaces
Hybrid Variational-Sampling Methods for Complex Posteriors
Message Passing Across Heterogeneous Graph Structures
Scalable Inference Under Model Misspecification
Causal Inference Through Graphical Constraint Propagation
Adaptive Belief Updating in Non-Stationary Networks
Exponential Family Approximations Beyond Mean-Field
Interacting Particle Systems for Intractable Likelihoods
Symmetry-Exploiting Inference in Structured Models
Differentiable Inference Engines for Learned Graphical Structure

All Computational Statistics PhD categories