Statistics for Applications MIT

 Education
This course offers an indepth the theoretical foundations for statistical methods that are useful in many applications. The goal is to understand the role of mathematics in the research and development of efficient statistical methods.

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1. Introduction to Statistics
In this lecture, Prof. Rigollet talked about the importance of the mathematical theory behind statistical methods and built a mathematical model to understand the accuracy of the statistical procedure.

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2. Introduction to Statistics (cont.)
This lecture is the second part of the introduction to the mathematical theory behind statistical methods.

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3. Parametric Inference
In this lecture, Prof. Rigollet talked about statistical modeling and the rationale behind statistical modeling.

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4. Parametric Inference (cont.) and Maximum Likelihood Estimation
In this lecture, Prof. Rigollet talked about confidence intervals, total variation distance, and KullbackLeibler divergence.

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5. Maximum Likelihood Estimation (cont.)
In this lecture, Prof. Rigollet talked about maximizing/minimizing functions, likelihood, discrete cases, continuous cases, and maximum likelihood estimators.

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6. Maximum Likelihood Estimation (cont.) and the Method of Moments
In this lecture, Prof. Rigollet continued on maximum likelihood estimators and talked about Weierstrass Approximation Theorem (WAT), and statistical application of the WAT, etc.