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This is course content of the Stochastic Modelling Course at VIA University College

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// repository documentation

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[Jump to Lesson Plan](#lesson-plan)

This repository contains resources for the [Stochastic Modelling and Processing](https://en.via.dk/tmh-courses/stochastic-modelling-and-processing) course at VIA University College, Horsens, Denmark.

The course consists of 10 scheduled sessions, each with a duration of 4 lessons. The sessions start in week 6 and the final session will be in week 17. Week 18 is left as a buffer. The course is academically demanding and requires the student to work on problems independently.

## Literature & Resources

ASPE: Montgomery, D.C. & Runger, G.C.. Applied Statistics and Probability for Engineers, 7th edition. All references are to chapters or exercises (found in the end of the book). Solutions to all exercises from the book are uploaded. You need to retrieve a copy however you usually retrieve books.

Non-session specific resources such as the exercises from the book, solutions, old exam cases, etc. can be found her:

[General Resources SMP](https://viaucdk-my.sharepoint.com/:f:/g/personal/rib_viauc_dk/Egbdbeb9oy1Oqk8hReXf2-wBibryPlLiVj2ujGdsvH5--w?e=liO02A)

NOTE: The previous exams are NOT representative of the one you will be completing and is used only as exercises since there are some overlaps in terms of the problems.

If/When you find mistakes in the solutions, please notify me so I can update them.

Wiseflow code for all flows that are used during the course is always 0000. This is not the code for the actual exam in June, though.

## Software

Make sure you install a working version of Jupyter Notebook and Python version 3.7 or higher. The easiest way to install Python and Jupyter is using [Anaconda Distribution](https://www.anaconda.com/products/distribution). You can choose whichever framework you want to work in as long as it can handle Jupyter Notebooks. Installing VS Code with a Jupyter Notebook extension seems to be a popular choice.

## Prerequisites

In the session plan below, a dedicated entry is made for prerequisites.

## Online Resources

The course is loosely built up around [H. Pishro-Nik's](http://www.ecs.umass.edu/pishro) [https://www.probabilitycourse.com/](https://www.probabilitycourse.com/)

I have compiled and uploaded all session from January 2021 to youtube. The link below will take you to a playlist containing all 10 sessions (theory only)

[Stochastic Modelling 2021 – All sessions](https://youtube.com/playlist?list=PL0bHDtI1FqFxv1ZxzsMn1sfYSxVSBbc05)

## Historical Notes

Stichastic Modelling and Processing was first offered in 2014 and has been scheduled 1-2 times per year since then. The course responsible is Richard Brooks (RIB) and has been the only lecturer teaching the course.

**Grade Distribution 2023 (ordinary exam only)**

| Grade | Count |
|-------|-------|
| 12    | 1     |
| 10    | 8     |
| 7     | 12    |
| 4     | 5     |
| 02    | 3     |
| 00    | 2     |
| -3    | 3     |

</div>

## Lesson Plan

Click on a session below to access a plan of a specific session and additional resources for that session.

| Session | Date                 | Topic                        |
| ------- | :----:               | ---------------------------- |
| 00      |                      | [Important Math Recap](/0_Important_MSE_Recap/README.md)                          |
| 01      | 9 Feb 08:20 – 11:50  | [Introduction + Recap Probability + Stochastic Variables](/01_Introduction_+_Recap_Probability_+_Stochastic_Variables/README.md) |
| 02      | 16 Feb 08:20 – 11:50 | [Discrete Random Variables](/02_Discrete_Random_Variables/README.md) |
| 03      | 23 Feb 08:20 – 11:50 | [Continuous Random Variables](/03_Continuous_Random_Variables/README.md) |
| 04      | 1 Mar 08:20 – 11:50  | [Multivariate Random Variables](/04_Multivariate_Random_Variables/README.md) |
| 05      | 8 Mar 08:20 – 11:50  | [Point Estimation and sampling](/05_Point_Estimation_and_sampling/README.md) |
| 06      | 15 Mar 08:20 – 11:50 | [Statistical Intervals](/06_Statistical_Intervals/README.md) |
| 07      | 22 Mar 08:20 – 11:50 | [Hypothesis Testing](/07_Hypothesis_Testing/README.md) |
| 08      | 12 Apr 08:20 – 11:50 | [Regression](/08_Regression/README.md) |
| 09      | 19 Apr 08:20 – 11:50 | [Introduction to Stochastic Processes](/09_Introduction_to_Stochastic_Processes/README.md)|
| 10      | 26 Apr  08:20 – 11:50 | [Markov Chains](/10_Markov_Chains/README.md) |
| 11      | 3 May  08:20 – 11:50 | [Recap and Exercises Markov Chains](/11_Recap_and_Exercises_Markov_Chains/README.md) |

</div>

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