UC Statistics equips students with probability, Bayesian methods, and machine learning. Collaborations span genomics, sports analytics, and fintech.
Hierarchical Bayesian model estimating excess mortality post-disasters
Causal inference using instrumental variables on education outcomes
Policy memo demystifying p-hacking and reproducibility for legislators
Interactive Shiny app teaching bootstrapping with real datasets
Machine-learning ensemble predicting NBA player performance metrics
Spatial kriging of groundwater contamination plumes
Differential-privacy mechanism balancing accuracy and confidentiality
Text-as-data sentiment analysis of central-bank statements
Deep-learning survival model for hospital readmission risk
Network community detection in social-media misinformation graphs
VR tutorial visualizing multivariate normal distributions
Adaptive clinical-trial design simulator for oncology drugs
Crowdsourced experiment on anchoring bias in online surveys
Critical essay on equity in predictive policing algorithm deployment
Time-series state-space model forecasting renewable energy outputs
Finite-mixture clustering of customer purchasing behavior
Workshop for nonprofits on evaluating program impact with R
Approximate Bayesian computation for epidemic parameter estimation
Anomaly detection in satellite telemetry using robust statistics
Interactive dashboard exploring election polling uncertainty
Master data, uncertainty, and insight with UC’s Statistics major.
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