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Probability and Statistics
Last Updated: 2026-02-05 16:29:35
Abstract
- Probability spaces- Discrete models, Randiom walk- Conditional probabilities, independence- Continuous models- Limit theorems- Methods of moments- Maximum likelihood estimation- Hypothesis testing- Confidence intervals- Introductory Bayesian statistics- Linear regression model
Objective
The first part of the course gives an overview of the main concepts needed to understand probability theory (sample spaces, discrete models, random walk, contiuous models and limit theorems such as the Laws of Large Numbers and the Central limit theorem). It will be based on the German script "Wahrscheinlichkeitsrechnung und Statistik". The second part covers some fundamental results of mathematical statistics including estimation methods, hypothesis testing as well as the linear regression model. For this part, we will use the script "Statistics for Mathematics". Both scripts are available at https://www.stat.math.ethz.ch/~fadouab/
Content
Probability: Chapters 1-5 (Probabilities and events, Discrete and continuous random variables, Generating functions) and Sections 7.1-7.5 (Convergence of random variables) from the book "Probability and Random Processes". Most of this material is also covered in Chap. 1-5 of "Mathematical Statistics and Data Analysis", on a slightly easier level. Statistics: Sections 8.1 - 8.5 (Estimation of parameters), 9.1 - 9.4 (Testing Hypotheses), 11.1 - 11.3 (Comparing two samples) from "Mathematical Statistics and Data Analysis".
Resources
Lecture Notes
(*) Wahrscheinlichkeitsrechnung und Statistik(*) Statistics for MathematicsBoth scripts can be found athttps://www.stat.math.ethz.ch/~fadouab/
Literature
A. DasGupta, Fundamentals of Probability: A First Course, Springer (2010) R. Berger and G. Casella, Statistical Inference, Duxbury Press (1990) J. A. Rice, Mathematical Statistics and Data Analysis, Wadsworth, second edition (1995) H.-O. Georgii, Stochastik, de Gruyter, 5. Auflage (2015) A. Irle, Wahrscheinlichkeitstheorie und Statistik, Teubner (2001)
General Information
- Language
- English
- Levels
- MSC
- Frequency
- Semesterly recurring
Examination
- Type
- session examination
- Mode
- written 180 minutes
- Aids
- None
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| revision course / private study |
Probability and Statistics
Self-study course. No presence required.
|
No time listed | 240 h semesterly |
Offered In
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Course Units for Additional Admission Requirements (The courses below are only available for MSc students with additional admission requirements.)
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Statistics Master (The following courses belong to the curriculum of the Master's Programme in Statistics. The corresponding credits do not count as external credits even for course units where an enrolment at ETH Zurich is not possible.)
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Course Units for Additional Admission Requirements (The courses below are only available for MSc students with additional admission requirements.)
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