Course description
Probability and Statistics
Dr. Anat Sakov
- Probability space. Conditional probability and Bayes rule.
Independence
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- Discrete random variables. Binomial, geometric and Poisson distributions. Poisson process.
- Continuous random variables. Exponential and normal distributions.
- Expectation and variance.
- Two-dimensional random variables. Correlation and covariance.
- Central limit theorem and law of large numbers.
- Principals in estimation. Maximum likelihood estimation and method of least squares.
- Principals of confidence intervals and statistical testing. Goodness of fit test.
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