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Computational Statistics

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Computational Statistics

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Research Frontiers in Approximate Bayesian Computation for Intractable Likelihoods

Statistical inference techniques for simulator-based models where likelihood functions are computationally intractable or unavailable.

Likelihood-Free Inference in High-Dimensional Parameter Spaces
Neural Density Estimation for Intractable Posterior Approximation
Adaptive Summary Statistics Discovery in ABC Methods
Amortized Bayesian Inference via Generative Models
Variance Reduction in Likelihood-Free MCMC Sampling
Implicit Model Inference Without Explicit Density Functions
Sequential Design Optimization for ABC Calibration
Topological Data Analysis in Approximate Likelihood Computation
Differentiable Simulators for Tractable Posterior Landscapes
Normalizing Flows for Intractable Likelihood Density Approximation

All Computational Statistics PhD categories