Andrew Rothman – Medium

Andrew Rothman

Statistical Learning Theory Part 6: Vapnik–Chervonenkis (VC) Dimension

Proof of Consistency, Rates, and Generalization Bounds for ML Estimators over Infinite Function Classes leveraging the VC-dimension

Nov 7, 2023

Statistical Learning Theory Part 6: Vapnik–Chervonenkis (VC) Dimension

Nov 7, 2023

Statistical Learning Theory Part 5: Shattering Coefficient

Proof of Consistency, Rates, and Generalization Bounds for ML Estimators over Infinite Function Classes leveraging the Shattering…

Nov 3, 2023

Statistical Learning Theory Part 5: Shattering Coefficient

Nov 3, 2023

Statistical Learning Theory Part 4: Finite Function Classes — Consistency, Rates, & Bounds

Proof of Consistency, Rates, and Generalization Bounds for ML Estimators over Finite Function Classes

Oct 31, 2023

Statistical Learning Theory Part 4: Finite Function Classes — Consistency, Rates, & Bounds

Oct 31, 2023

Statistical Learning Theory Part 3: Consistency of Machine Learning Estimators

Conditions for Convergence and Consistency of Learned ML Estimators

Oct 28, 2023

Statistical Learning Theory Part 3: Consistency of Machine Learning Estimators

Oct 28, 2023

Statistical Learning Theory Part 2: Optimality of the Bayes Classifier

Motivation and Proof of Optimality of the Bayes Classifier

Oct 24, 2023

Statistical Learning Theory Part 2: Optimality of the Bayes Classifier

Oct 24, 2023

Statistical Learning Theory Part 1: Hoeffding’s Inequality Derivation & Simulation

Derivation of Hoeffding’s Inequality from first principles

Oct 20, 2023

Statistical Learning Theory Part 1: Hoeffding’s Inequality Derivation & Simulation

Oct 20, 2023

Published in
TDS Archive

Causal Inference in Data Science: Conditional Independence via Propensity Score Adjustment

Specification and Proofs of Propensity Scores, with Accompanied Computational Simulation

Oct 8, 2021

Causal Inference in Data Science: Conditional Independence via Propensity Score Adjustment

Oct 8, 2021

Published in
TDS Archive

Parametric Bayesian Inference: Implementation of Numerical Sampling Techniques with Proofs

Acceptance/Rejection Sampling & MCMC Metropolis-Hastings Sampling with full Computational Simulation

Sep 27, 2021

Parametric Bayesian Inference: Implementation of Numerical Sampling Techniques with Proofs

Sep 27, 2021

Published in
TDS Archive

Mathematical Statistics — A Rigorous Derivation and Analysis of the Wald Test, Score Test, and…

Derivations of the Classic Trinity of Inferential Tests with full Computational Simulation

Sep 25, 2021

Mathematical Statistics — A Rigorous Derivation and Analysis of the Wald Test, Score Test, and…

Sep 25, 2021

Published in
TDS Archive

Mathematical Statistics: Mathematical Justifications for Maximum Likelihood Estimation

Specification and Derivations of Connections between Total Variation, KL Divergence, and MLE

Sep 23, 2021

Mathematical Statistics: Mathematical Justifications for Maximum Likelihood Estimation

Sep 23, 2021

Andrew Rothman

Andrew Rothman

Principal Data/ML Scientist @ The Cambridge Group | Harvard trained Statistician and Machine Learning Scientist | Expert in Statistical ML & Causal Inference

Following

Help
Status
About
Careers
Press
Blog
Privacy
Terms
Text to speech
Teams