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Laboratory of Statistics
Prerequisites
Prerequisites of this Course
Lectures
1. Basics of Python Programming
1.10. Examples: Lecture 1
1.11. Exercises for Lecture 1
1.12. Solutions: Lecture 1
2. Notable Python Libraries
2.4. Examples: Lecture 2
2.5. Exercises for Lecture 2
2.6. Solutions: Lecture 2
3. Data Visualisation with Python
3.6. Examples: Lecture 3
3.7. Exercises for Lecture 3
3.8. Solutions: Lecture 3
4. Generation of Pseudo-Random Numbers
4.6. Examples: Lecture 4
4.7. Exercises for Lecture 4
4.8. Solutions: Lecture 4
5. Some python insight: classes,
lambda
functions, functional programming
5.4. Examples: Lecture 5
5.5. Exercises for Lecture 5
5.6. Solutions: Lecture 5
6. Finding Zeros and Extrema
6.4. Examples: Lecture 6
6.5. Exercises for Lecture 6
6.6. Solutions: Lecture 6
7. The Poisson Distribution
7.5. Exercises for Lecture 7
7.6. Solutions: Lezione 7
8. Toy Experiments and Integration using Pseudorandom Numbers
8.3. Examples: Lecture 8
8.4. Exercises for Lecture 8
8.5. Solutions: Lecture 8
9. The likelihood
9.7. This is a notebook example
9.8. Drawing of a function
9.9. Accessing files on disk
9.10. Exercises for Lecture 9
9.11. Likelihood exercise solutions
9.12. Solution Libraries
10. Parameter Estimate using the Maximum Likelihood Method
10.10. Exercises for Lecture 10
10.11. Maximum likelihood exercise solutions
11. Least-Squares Fitting
11.5. Exercises for Lecture 11
11.6. Least squares solutions
12. Fit of Binned Distributions
12.7. Exercises for Lecture 12
12.8. Binned fits solutions: Exercise 12.1
12.9. Binned fits solutions: Exercise 12.2
12.10. Binned fits solutions: exercise 12.3
12.11. cost functions comparison: exercise 12.4
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