Probability Theory and Statistics with Python
At the moment of writing, I am 20 years old software engineer and computer science student. In the last two years, I learned more than during all my conscious living before that. One of the most important things that I realized being a student and software engineer was the importance of a deep understanding of the basic concepts of complex subjects. Especially in those, you spend life doing. Once in university was a course about probability theory. In the process of learning, I left notes and visualizations in the Jupiter notebook. The goal was to build a good understanding of basics while making real word applications modeling. After some time, I realize that my drafts may be better than traditional formal representation, which we usually see in a university. Therefore I moved my notes to the blog.
Table of Content
3. The Law Of Total Probability
6. Random Variable, Distribution of the Discrete Random Variable
8. Probability Density Function
9. Expected Value, Mode, Median
10. Moments, Variance, Standard Deviation
18. Multivariate Random Variable - the Distribution Function
19. Multivariate Random Variable - Probability Density
20. Multivariate Random Variable - Systems of Random Variables
21. Multivariate Random Variable - Numerical Characteristics
22. Law of Large Numbers and Chebyshev’s Inequality
24. Empirical Distribution Function
25. Histogram
26. Numerical Characteristics for Statistical Distribution
27. Inferential Statistics and Point Estimation
29. Maximum Likelihood Estimation
31. Hypothesis Testing and Power of the Test
32. Pearson’s Chi-Squared Test