UC’s MS in Statistics provides rigorous training in probability, inference, and modern computational methods, preparing students for data-science leadership and doctoral research.
Hierarchical Bayesian model estimating COVID-19 excess mortality by county
Sparse Gaussian-process emulator for climate model downscaling
Causal forest analysis of education policy impacts on earnings trajectories
Tensor decomposition of single-cell RNA-seq count matrices
Empirical likelihood methods for survey data with non-response bias
Deep generative model producing privacy-preserving synthetic health records
Sequential Monte Carlo algorithm accelerating portfolio risk estimation
Network meta-analysis of dietary interventions on lipid outcomes
Adaptive design simulation for platform clinical trials
Policy brief explaining p-value pitfalls to journalists
Functional data analysis of wearable sensor gait waveforms
Change-point detection in satellite fire-radiative power time series
Approximate Bayesian computation for ancient DNA demographic models
Workshop teaching tidyverse and ggplot through environmental datasets
White paper on statistical standards for AI fairness audits
Interactive Shiny app visualizing bootstrap distributions
Markov chain mixing diagnostics for high-dimensional Hamiltonian Monte Carlo
Student-run consulting clinic projects for nonprofits lacking data capacity
VR tutorial demonstrating central-limit theorem through particle simulation
Conference on reproducible research and open statistical software
Model uncertainty and extract insight with UC’s computation-rich MS.
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