Course Details

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Course URL (e.g., on e-class)

Learning Outcomes (English) This course is designed to serve as a first undergraduate course in probability theory and to introduce students in the notion and logic of probability. This course hopes to provide students with a solid foundation in probability theory that can serve as a basis for the rest courses.

General Competencies (English) Search, analysis and synthesis of data and information Adaptation in new conditions Decision Making Independent work Work at an interdisciplinary framework Formulation of new research ideas Promoting reasoning and self improvement Promoting free, creative and deductive reasoning

Course Content (English) 1. Random Experiments, Sample space, Sample events. Finite sample spaces, Classical Probability, Axiomatic foundation of Probability 2. Conditional Probability and Stochastic Independence of Events and Experiments 3. Random Variables, Distribution Function, Probability Function, Probability Density Function 4. Distribution of a Function of a Random Variable, Expected Value, Variance of a Random Variable, Moments of a Random Variable 5. Discrete and Continuous Random Variables, Some Basic Univariate Discrete Distributions 6. Applications of Discrete Distributions to Computer Science and Telematics 7. Continuous Random Variables. Some Basic Univariate Continuous Distributions 8. Applications of Continuous Distributions to Computer Science and Telematics 9. Characteristic Function, Random Vectors, Distribution of Random Vectors, Functions of Random Vectors 10. Conditional Distributions and Moments of Conditional Distributions 11. Sequences of Random Variables 12. Convergence of Distributions, Central Limit Theorem.

Use of ICT (English) Support the learning process through the electronic platform e-class

Is it elective? Άγνωστο Ναι Όχι

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