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Sequential Variational Inference Experiments
As part of a DATA5925 group research project, I contributed Python experiments for sequential variational inference. The work starts from an exact Bayesian linear-regression baseline and compares batch orders, mean-field and full-covariance approximations, and Gaussian and logistic-regression settings.
My contribution includes reproducibility checks and divergence diagnostics to make the experiment comparisons easier to inspect and repeat.
This example demonstrates statistical programming and reproducible research. It is group coursework, not a publication, a clinical model or an independently delivered customer project. The source repository remains private; this portfolio description shares a non-confidential overview only.
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