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KL Divergence and Wasserstein Distance: Two Different Notions of Distributional Difference
Comparing probability distributions is deceptively difficult because there is no single universal notion of what it means for two distributions to be close. ...
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Importance Sampling: Change of Measure, Variance, and Proposal Design
Importance sampling is one of the most useful examples of a simple probabilistic identity becoming a powerful computational method. The central idea is to es...
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Survival Analysis: Censoring, Hazards, and Time-to-Event Models
Survival analysis is the statistical study of time until an event occurs, but that familiar description understates what makes the subject distinctive. The c...
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Stratified Sampling
Abstract
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GDP Data Analysis: Measurement, Revisions, and Limits
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Kernel K-Means in R: Geometry Before Clusters
Kernel k-means replaces Euclidean distances in the original input space with squared distances in an implicit feature space.
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Understanding t-SNE Without Overinterpreting the Map
t-SNE is a visualization method, not a general-purpose clustering algorithm and not a faithful low-dimensional reconstruction of all high-dimensional geometry.
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Forecast Accuracy Is Not Inventory Performance
Forecasting metrics evaluate predictions. Supply chains pay for decisions. A model can achieve a lower RMSE and still produce higher stockout and inventory c...
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A Technical History of Artificial Intelligence
The history of artificial intelligence is often told as a straight line from ancient automata to modern language models. That makes a good story but a poor t...
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Climate Financial Risk Beyond Traditional VaR
Climate financial risk is better treated as scenario-conditioned loss analysis than as a simple extension of short-horizon market VaR.
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