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Monte Carlo Accuracy Is About Variance, Not Just Samples
The familiar square-root convergence rate does not mean that all Monte Carlo estimators are equally useful. Good estimator design can reduce variance by fact...
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Multivariate Time Series Forecasting: VAR and VECM Models Explained
A practical guide to VAR and VECM for multivariate time series forecasting, including math, assumptions, cointegration testing, and Python code.
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Survival Analysis in Public Policy and Government: Applications, Methodology, and Implementation
Survival analysis offers a powerful framework for analyzing time-to-event data in public policy, enabling data-driven decision making across health, welfare,...
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Why Longer Survival After Diagnosis Can Mislead
Survival after diagnosis depends on when the clock starts and who enters the denominator. A worked cohort separates those changes from an actual reduction in...
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A Meta-Analysis Is Not a Magic Upgrade
A pooled estimate can be very precise and still be scientifically weak. The quality of a meta-analysis depends on the studies that entered it, the question t...
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Group Averages: What Store-Level Data Cannot Tell You About Customers
The store-level scatter is beautiful: a correlation of 0.77 across a thousand stores, with a confidence interval you could measure with a ruler. The customer...
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Consistency Regularisation Is an Invariance Assumption, Not Free Supervision
Consistency regularisation is often presented as a way to extract supervision from unlabelled data by requiring stable predictions under perturbation. The su...
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The Role of Reinforcement Learning in Optimizing Maintenance Strategies: Dynamic Predictive Maintenance Through Reward-Based Learning
Reinforcement Learning (RL) brings intelligent autonomy to industrial maintenance, enabling dynamic optimization through trial-and-error interaction with com...
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Why SMOTE Isn't Always the Answer
SMOTE generates synthetic samples to rebalance datasets, but using it blindly can create unrealistic data and biased models.
Read articleEntropy Minimisation Can Make the Wrong Answer More Confident
Entropy minimisation encourages decisive predictions on unlabelled data. That can be useful when decision boundaries should avoid high-density regions, but l...
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