### General Information

Course Code | E_EOR1_PT |
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Credits | 6 EC |

Period | P1+2 |

Course Level | 100 |

Language of Tuition | English |

Faculty | School of Business and Economics |

Course Coordinator | dr. D.A. van der Laan |

Examiner | dr. D.A. van der Laan |

Teaching Staff |
dr. D.A. van der Laan prof. dr. B.F. Heidergott |

### Practical Information

You need to register for this course yourself

Teaching Methods | Study Group, Lecture |
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Target audiences

This course is also available as:

### Course Objective

The learning objectives are as follows:1. Knowledge and understanding of basic concepts of probability theory

and probability distributions.

2. Selecting an appropriate method to solve typical problems in

probability theory and applying these methods correctly.

3. Translating verbally described problems in the language of

probability theory to solve the problem.

4. Knowledge of probability distributions which occur most commonly in

practice and being able to use the known properties of these

distributions to solve problems.

### Course Content

The mathematical foundation for both modeling decisions underuncertainty when performing statistics is probability theory and

probability theory therefore has a central role in the bachelor

Econometrics and Operations Research.

The structure of this course is as follows.

• Basic elements of the probability account (random experiment, outcomes

space, eventuality and opportunity) and fundamental calculation rules

for opportunities on eventualities. Combinatorial probability models,

conditional probabilities, the rule of Bayes, and the law of total

probability.

• Introduction of the concept of a stochastic variable, and concepts

such as distribution function, probability function, expectation and

variance of a stochastic variable.

• Specific discrete probability distributions, such as the binomial,

hypergeometric, Poisson, and geometric distribution.

• Continuous stochastic variables and associated probability

distributions. Specific continuous probability distributions such as the

uniform, normal, exponential and gamma distribution. Relations between

these continuous probability distributions and the previously introduced

discrete probability distributions are discussed.

• If there is sufficient time at the end of the course, the concepts of

bivariate and multivariate probability distribution included related

concepts such as joint and marginal distribution functions, conditional

probability distribution and independence of stochastic variables.

### Teaching Methods

Theory lectures + Tutorials.### Method of Assessment

Interbetween exam - individual markFinal exam - individual mark

Individual assignment - individual mark

### Literature

Author: Saeed Ghahramani; Titel: Fundamentals of Probability (withstochastic processes); Fourth Edition; publication year: 2018;

publisher: CRC Press, Taylor&Francis Group; EAN: 9781498755092

### Additional Information

During this course some knowledge gained at the simultaneous courseAnalysis I will be utilized.