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Probability Integral Transform: Theory and Applications
An in-depth guide to understanding and applying the Probability Integral Transform in various fields, from finance to statistics.
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Understanding Probability and Odds
Discover the difference between probability and odds in biostatistics, and how these concepts apply to data science and machine learning. A clear explanation...
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Credit-Score Gini: Ranking Is Not Calibration
In binary credit scoring, the model Gini is a linear transformation of ROC AUC. It measures ranking discrimination, not calibration, default rate, or economi...
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Bhattacharyya Distance: Measuring Distribution Overlap
Bhattacharyya distance measures overlap between probability distributions. It should be treated separately from predictive loss functions such as squared err...
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Markov Chains: State, Recurrence, Stationarity, and Mixing
A Markov chain is a stochastic process whose future evolution is conditionally independent of the distant past once the present state is known. That sentence...
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Regularization: Geometry, Bias, and Statistical Control
Regularization is often introduced as a practical device for preventing overfitting, but that description is too shallow to explain what the method actually ...
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Feature Engineering: Representation, Leakage, and Validation
Feature engineering is often described as the process of creating variables that help a machine-learning model predict more accurately. That description is c...
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AI Fairness: Metrics, Trade-offs, Causal Assumptions, and Governance
Fairness in machine learning is often presented as though it were a technical property that can be measured once the model has been trained. That framing is ...
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Detecting Multivariate Data Drift: From Distribution Shift to Model Risk
Multivariate data drift is a change in the joint distribution of model inputs. Detecting it requires tests that respect dependence structure, representation,...
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P-Values: Sampling Models, Evidence, and Statistical Decisions
Few statistical quantities are used as widely, or interpreted as carelessly, as the p-value. The problem is not that the definition is especially complicated...
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