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

5 min readNov 7, 2023

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

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Andrew Rothman

Statistical Learning Theory Part 5: Shattering Coefficient

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

5 min readNov 3, 2023

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Statistical Learning Theory Part 5: Shattering Coefficient

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Andrew Rothman

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

4 min readOct 31, 2023

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

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Andrew Rothman

Statistical Learning Theory Part 3: Consistency of Machine Learning Estimators

Conditions for Convergence and Consistency of Learned ML Estimators

3 min readOct 28, 2023

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Statistical Learning Theory Part 3: Consistency of Machine Learning Estimators

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Andrew Rothman

Statistical Learning Theory Part 2: Optimality of the Bayes Classifier

Motivation and Proof of Optimality of the Bayes Classifier

3 min readOct 24, 2023

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Statistical Learning Theory Part 2: Optimality of the Bayes Classifier

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Andrew Rothman

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

Derivation of Hoeffding’s Inequality from first principles

7 min readOct 20, 2023

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

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Andrew Rothman
in
Towards Data Science

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

Specification and Proofs of Propensity Scores, with Accompanied Computational Simulation

8 min readOct 8, 2021

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

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Andrew Rothman
in
Towards Data Science

Parametric Bayesian Inference: Implementation of Numerical Sampling Techniques with Proofs

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

7 min readSep 27, 2021

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Parametric Bayesian Inference: Implementation of Numerical Sampling Techniques with Proofs

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Andrew Rothman
in
Towards Data Science

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

9 min readSep 25, 2021

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

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Andrew Rothman
in
Towards Data Science

Mathematical Statistics: Mathematical Justifications for Maximum Likelihood Estimation

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

3 min readSep 23, 2021

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Mathematical Statistics: Mathematical Justifications for Maximum Likelihood Estimation

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Andrew Rothman

Andrew Rothman

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

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