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Computational Statistics

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Computational Statistics

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Research Frontiers in Importance Sampling Methods and Adaptive Schemes

Development of adaptive importance sampling distributions and reweighting techniques to reduce variance in Monte Carlo estimation.

Adaptive Tempering in High-Dimensional Posterior Exploration
Self-Normalizing Importance Weights Under Model Misspecification
Entropy-Driven Proposal Design for Rare Event Simulation
Manifold-Aware Importance Sampling in Latent Variable Models
Sequential Allocation Strategies for Variance Reduction Optimization
Kernel Adaptive Schemes in Non-Stationary Monte Carlo
Importance Sampling Collapse Prevention via Geometric Learning
Hybrid Tempering Methods for Multimodal Posterior Landscapes
Multifidelity Importance Sampling with Information Reuse
Neural Proposal Adaptation for Intractable Likelihood Problems

All Computational Statistics PhD categories