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The intention of this course is to provide students with decision theory, estimation, confidence intervals, and hypothesis testing. Topics will be covered in this course are statistical models, Bayesian models, decision-theoretic framework, prediction, and sufficiency. Addition to that, it introduces large sample theory, asymptotic efficiency of estimates, and sequential analysis, exponential families I, exponential Families II, methods of estimation I and methods of estimation II.


This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer needs to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.


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Course Credit: MIT

Course Curriculum

Module: 01
Course Overview 00:10:00
Statistical Models 00:10:00
Bayesian Models 00:10:00
Decision Theoretic Framework 00:10:00
Prediction 00:15:00
Sufficiency 00:10:00
Exponential Families I 00:10:00
Module: 02
Exponential Families II 00:10:00
Methods of Estimation I 00:10:00
Methods of Estimation II 00:10:00
Bayes Procedures 00:10:00
Minimax Procedures 00:10:00
Unbiased Estimation and Risk Inequalities 00:10:00
Convergence of Random Variables Probability Inequalities 00:10:00
Module: 03
Limit Theorems 00:15:00
Asymptotics I: Consistency and Delta Method 00:10:00
Asymptotics II: Limiting Distributions 00:10:00
Asymptotics III: Bayes Inference and Large-Sample Tests 00:15:00
Gaussian Linear Models 00:15:00
Generalized Linear Models 00:10:00
Case Study: Applying Generalized Linear Models 00:15:00
Submit Your Assignment 00:00:00
Certification 00:00:00

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