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Time series data is often composed of information at multiple time scales, particularly in biomedical data.

Quantifying the difference between two probability distributions is a fundamental problem in machine learning, and there a couple common ways to quantify that difference.

Rationale # Seizure forecasting algorithms could play a vital role in managing epilepsy by providing timely warnings and enabling proactive interventions; however, algorithms have historically struggled when translated to unseen, real-time data.

Large language models (LLMs) have seen recent spikes of popularity due to increasing quantities of training data and powerful compute.

Feb 2022: # 2022-02-09: # Now that we found the closed form solution for \( \hat{\beta} \) we can find it's mean and variance.