Checking date: 24/04/2024

Course: 2024/2025

Probability
(17753)
Master in Statistics for Data Science (Plan: 386 - Estudio: 345)
EPI

Coordinating teacher: STRZALKOWSKA-KOMINIAK , EWA

Department assigned to the subject: Statistics Department

Type: Compulsory
ECTS Credits: 3.0 ECTS

Course:
Semester:

Objectives
The course Objectives are: 1. Understand the fundamental concepts of probability and random experiments. 2. Analyze events and calculate probabilities using various techniques. 3. Explore conditional probability and apply Bayes' formula. 4. Recognize independence in random events and perform combinatorial analysis. 5. Define discrete random variables and analyze their properties. 6. Examine different discrete probability distributions (Binomial, Geometric, Poisson, etc.). 7. Introduce continuous random variables and study their characteristics. 8. Analyze continuous probability distributions (Uniform, Exponential, Normal, etc.). 9. Understand and work with random vectors, including joint, marginal, and conditional distributions. 10. Investigate properties of random vectors, including independence and transformations. 11. Explore the concepts of sums, mixtures, and random samples. 12. Analyze the concept of order statistics in random samples. 13. Study the properties of expectation, covariance, conditional expectation, and variance. 14. Examine moment generating functions. 15. Explore limit theorems such as Markov and Chebyshev inequalities. 16. Understand convergence in probability, almost sure convergence, and convergence in distribution. 17. Apply the Central Limit Theorem to analyze the behavior of sample means.
Skills and learning outcomes
Description of contents: programme
1. Random experiments 1.1 Events 1.2 Probability 1.3 Conditional probability 1.4 Bayes' formula 1.5 Independence 1.6 Combinatorics 2. Discrete random Variables 2.1 Definition of random variable 2.2 Probability mass function and cumulative distribution function 2.3 Mean, variance, and quantiles 2.4 Binomial, Geometric, Poisson, Negative Binomial, and Hypergeometric distributions 3. Continuous random variables 3.1 Density mass function and cumulative distribution function 3.2 Mean, variance, and quantiles 3.3 Transformations of a random variable 3.4 Uniform, Exponential, Normal, Gamma, and Beta distributions 4. Random vectors 4.1 Joint distributions, marginal distributions, and conditional distributions 4.2 Independence 4.3 Transformations of random vectors 4.4 Multivariate Normal and Multinomial distributions 4.5 Sums of random variables 4.6 Mixtures 4.7 General concepto of random variable 4.8 Random sample 4.9 Order statistics 5. Properties of the expectation 5.1 Expectations of sums of random variables 5.2 Covariance 5.3 Conditional expectation 5.4 Conditional variance 5.5 Moment generating function 6. Limit Theorems 6.1 Markov and Chebishev inequalities 6.2 Weak Law of Large Numbers (convergence in probability) 6.3 Strong Law of Large Numbers (almost sure convergence) 6.5 Central Limit Theorem (convergence in distribution)
Learning activities and methodology
TRAINING ACTIVITIES OF THE STUDIES PROGRAM AF1 Theory class AF2 Exercises class AF4 Lab class AF5 Office hours AF6 Group office hours AF7 Individual student's work AF8 In-class assessment activities Activity Code Total nb hours Total in-class nb hours % in-class hours AF1 33 33 100 AF2 15 15 100 AF4 15 15 100 AF5 12 12 100 AF6 30 0 0 AF7 115,5 0 0 AF8 4,5 4,5 100 COURSE TOTAL 225 75 33 TEACHING METHODOLOGIES USED IN THIS COURSE MD1 Teacher class presentations with visual support to present the main contents of the course. MD3 Exercises and case studies to be solved individually or in group
Assessment System
• % end-of-term-examination 50
• % of continuous assessment (assigments, laboratory, practicals...) 50
Calendar of Continuous assessment
Basic Bibliography
• Sheldon Ross. A First Course in Probability. Pearson Prentice Hall. 2010