# Stochastic Modelling and Processing

## Code

IT-SMP1

## Version

1.3

## Offered by

ICT Engineering

## ECTS

5### Prerequisites

Upper level mathematics equivalent to A-levels. Calculus.

There is a limit of 45 participants in the course. In the event that more than 45 students select the course, we will select the 45 students based on their grades in MSE1 or other equivalent math courses.

### Main purpose

### Knowledge

- The main working tools and concepts of stochastic modelling
- Probability theory and distributions
- Confidence Intervals and Hypothesis Testing
- Inferential statistics

### Skills

- Apply results from basic probability theory including conditional probability
- Use probability density and distributions functions of one and two variables
- Account for random variables and random processes
- Calculate and estimate errors and uncertainties.

### Competences

- Planning experiments and state hypothesis
- Presenting statistical results from experiments
- Modelling experimental data with regression
- Analysing experimental results and test hypotheses

### Topics

- Experiments and the concepts of probability
- Calculations of probability
- Often encountered probability density and distribution functions
- Random variables and random processes
- Analysis of errors in experiments
- Design of statistical experiments
- Creating hypotheses and confidence intervals
- Presentation of statistical data
- Linear and exponential regression

### Teaching methods and study activities

### Resources

*Applied Statistics and Probability for Engineers*, 4th edition Wiley (obtained from library)

*Applied Statistics and Probability for Engineers*, 7th edition Wiley (e-book)

### Evaluation

### Examination

The final exam is a 3 hour written exam and takes place at Campus Horsens. All supplementary materials and aids are allowed, e.g. using a computer as a reference work.

Communication of any sort is not allowed during the exam and will lead to

expulsion of all involved parties from the exam.

The exam must be completed in the Jupyter Notebook environment and the answers must be submitted in Wiseflow.

The re-exam may be held as an oral examination.

### Grading criteria

Mark 12:

Awarded to students who have shown excellent comprehension of the above-mentioned competences. A few minor errors and shortfalls are acceptable.

Mark 02:

Awarded to students for the just acceptable level of comprehension of the required competences.

### Additional information

### Responsible

Richard Brooks

### Valid from

8/1/2019 12:00:00 AM

### Course type

6. semester

7. semester

Elective for the specialization Data Engineering

Electives

### Keywords

<div class="ExternalClassACD9CF34902F49C5B6020047359144F8">Experiments and the concepts of probability, mathematical models based on random variation </div>