Stochastic Processes - Computational Exercises

This repository hosts the exercises for the course of Stochastic Processes taught by Dr. Michail Loulakis and Dr. Aristides Doumas at the National Technical University of Athens.

The main site of the course, where you can find the book together with other relevant material can be found here For a Greek version of the instruction guide check out Odigies.pdf. For an English version of the lab, replace the notebooks in the main directory with those in the English_version directory.

Contents

• [Getting Started](#Getting Started)
  • [Installation Instructions](#Installation Instructions)
• Python
• Markdown
• [Jupyter Notebook](#Jupyter Notebook)
  • [Launching a Notebook](#Launching a Notebook)
• [Working on Exercises](#Working on Exercises)

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Getting Started

To complete the exercises you need to install Python version 3 together with a few non-standard libraries and the Jupyter Notebook app.

Below are some simple instruction on how to install all of them.

Installation Instructions

The recommended and easiest way to install everything at once is through Anaconda. Anaconda is a cross-platform (Windows, Mac, GNU/Linux) python distribution tailored for data science.

To install Anaconda just follow the link provided and select the Python 3 installer that corresponds to your OS.

If for some reason you don't want to install Anaconda, then you must first install Python 3, either from the official repository or your OS package manager. If you are using OSX or GNU/Linux then you probably have Python pre-installed. However, you must make sure that you have Python 3 not 2.

Then you have to install the required packages specified in requirements.txt. The most sane approach to do this is though your OS package manager. If for some reason you cannot use a native package manager, then you should use Python's package manager pip. To install pip (you need to install Python first) check out this link.

To install everything through pip run one of the following commands in your terminal:

# if you only have python 3 you can just write python
python3 -m pip install -r requirements.txt
# or if you don't have admin rights
python3 -m pip install --user -r requirements.txt

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Python

The language of choice for these exercises is Python. Do not be dishearten if you are not familiar with it! Python's greatest advantage is that it easy to learn and read. For these exercises, we will make use of Python's most basic scientific libraries:

1. Numpy for numerical programming
2. Matplotlib for visualization

other libraries may be used for some exercises but only to enhance the learning experience and you won't have to interact with them (unless you want to!).

If you are not familiar with Python or with any of these 2 libraries, you can get started by completing the first 2 (freely available) courses provided by DataCamp:

1. Intro to Python
2. Intermediate Python

Python is an interpreted language, so you can fire-up its terminal and start giving commands to the computer. This can be useful to experiment with code but in order to submit the exercises you need to write code the code cell of a Jupyter notebook (see [Jupyter Notebook](#Jupyter Notebook)). If you want to experiment with the terminal, we suggest you use the IPython terminal or qtconsole (both installed with Anaconda).

Markdown

The exercises do not only require you to code but also explain what you are doing and answers some question in free-form text. To do so you have to write regular text not only comments. To format your text properly you must learn the markdown syntax. Again, no need to be overwhelmed, this is by far the easiest syntax you will ever have to learn!

A brief overview of all the relevant features can be found here. For extra examples you can look here and here. Also if you click Enter on any "markdown-cell" of the provided notebooks, you can see the source code an get an idea of how it is working.

Jupyter Notebooks

The exercises are organized in Jupyter Notebooks. The notebook is:

a web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text

For an introductory video about working with Jupyter Notebooks follow this link.

A Jupyter Notebook is organized in cells. Every cell is either a code-cell or a markdown-cell. In the code-cells you write pure Python code (no adjustments needed) that executes and the output (if visible) is displayed below the cell. In the markdown-cells you write plain-text following the markdown syntax that will then be rendered as a rich-format text. In short, it brings together Python and Markdown in order to produce self-contained reports.

Launching a Notebook

To view and interact with the notebook first you need to install jupyter and launch a notebook from inside the main folder of this repository. To do that:

• Via terminal:
  1. Start a new terminal (in Windows with Anaconda use the "Anaconda prompt")
  2. navigate the repository folder (cd path/to/folder)
  3. issue the command jupyter notebook
• Via Anaconda Navigator:
  1. Launch the Anaconda navigator.
  2. Select and launch the Jupyter Notebook app.
  3. Navigate to the repository folder.
• For Windows with Anaconda:
  1. Make a shortcut of the Jupyter Notebook app to your desktop (copy from Start-up menu)
  2. Right-click on the new launcher and change the “Start in” field by pasting the full path of the repository folder.
  3. Double-click on the Jupyter Notebook desktop launcher to start the Jupyter Notebook App (note that a terminal will also open).

In all cases, a new page or tab will open in the browser. If this doesn't happen, or you accidentally close the window, you can navigate there manually by typing the following address: http://localhost:8888/

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Working on Exercises

To work on the exercises you need to download (and unzip) this repository and launch the jupyter notebook from inside the main folder.

Once the main page opens in the browser you can select the exercise you want to work on and start working. Once you open the .ipynb file a new tab or page will open where a problem is described and (usually) a sample code is given. Your task is to answer the given question via text and/or code.

Once your are satisfied with your answer convert your notebook to HTML (File > Download as... > HTML) and email the html file to Prof Loulakis.

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