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Inflammation Is Not a Diagnosis
"Inflammation" has become a universal explanation online. In biology it is a family of processes that differ by tissue, trigger and duration, and its most us...
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When One Simulation Metric Tells the Wrong Story
A parameter setting can have the smaller typical estimation error and still be much more sensitive to starting values. Another can look poor under a toleranc...
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Sample Ratio Mismatch: The One Diagnostic That Invalidates an Experiment
The experiment assigned a million users evenly and logged 49.75 percent in the treatment arm. That is 2,470 users missing, a one-in-a-million coincidence, an...
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Seed Oils, Inflammation and the Difference Between Chemistry and Clinical Evidence
The argument against seed oils begins with real chemistry: polyunsaturated fats can oxidise and linoleic acid participates in omega-6 metabolism. The error i...
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Distribution-Free Semi-Supervised Learning Is Not Assumption-Free
Hirose, Irobe and Kanamori propose a generalized risk-rewriting framework for semi-supervised learning that avoids cluster, manifold and augmentation assumpt...
Read articleDetox Is a Vague Claim Until the Toxin Is Named
The body does detoxify. That fact does not validate detox products. Real toxicology identifies the compound, dose, body burden and elimination pathway; welln...
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Speculative Decoding Does Not Mean Approximate Generation
Speculative decoding uses a cheaper model to propose several future tokens and a larger target model to verify them in parallel. In the standard algorithm, t...
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Information Geometry for Data Science: Curvature, Models, and Learning
Information geometry treats probability models as geometric objects, making it easier to reason about distance, curvature, uncertainty, and learning.
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Discrete Mathematics for Data Science: States, Constraints, and Algorithms
Discrete mathematics is the part of mathematics that explains how data systems make decisions, count possibilities, represent relationships, and enforce cons...
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Bayesian Decision Theory for Data Science: From Uncertainty to Action
Bayesian decision theory connects statistical uncertainty to action by asking not only what is likely, but what decision is best under uncertainty.
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