*Here, you find my whole video series about Probability Theory in the correct order and I also help you with some text around the videos. If you want to test your knowledge, please use the quizzes, and consult the PDF version of the video if needed. When you have any questions, you can use the comments below and ask anything. However, without further ado let’s start:*

### Introduction

**Probability Theory** is a video series I started for everyone who is interested in stochastic problems and statistics. We can use a lot of results that one can learn in measure theory series. However, here we will be able to apply the theorems to probability problems and random experiments. In order to this, we will use RStudio along the way:

Click here to find the codes we used in R. With this you now know the topics that we will discuss in this series. Some important bullet points are **probability measures**, **random variables**, **central limit theorem** and **statistical tests**. In order to describe these things, we need a good understanding of measures first. They form the foundation of this probability theory course but we do not need to go into details. Now, in the next video let us discuss **probability measures**.

### Let’s get started

The notion of a **probability measure** is needed to describe stochastic problems:

We distinguish **discrete** and **continuous** cases because they often occur in applications:

Now we talk about a special discrete model: the **binomial distribution**. It occurs when we toss a coin n times and count the heads. Alternatively, we could draw n balls, with replacement, from a urn with two different kinds of balls:

Now we talk about **product spaces**, which will be very important for constructions of probability spaces:

The next discrete model we will discuss is the so-called **hypergeometric distribution**. It is related to the binomial distribution in an urn model. However, now we will draw **without replacement**.

In the next video, we start with a very important topic: **conditional probability**.

Now we are ready to discuss a famous theorem: **Bayes’s theorem**. We also talk about the related law of total probability and illustrate both things with the popular Monty Hall problem.

Next, we talk about an important concept: **independence**. We start by explaining the independence of events. First we just have two events but then we consider infinitely many.

We are ready to introduce **random variables**. It turns out that the definition is not complicated at all. Nevertheless, we often use them to extract the important parts of a random experiment.

Next, we want to introduce the notion of **distribution of a random variable**. This is not a complicated concept but, in fact, it will be crucial in all upcoming videos.

We continue with the **cumulative distribution function** for a random variable. It is often just called CDF.

Now let us define the notion of **independence for random variables**. We will use the definition of independence for events for this:

The next concept is one of the most important ones. We talk about the **expectation** of a random variable. You also find a lot of other names for that, for example, **expected value**, **first moment**, **mean**, and **expectancy**.

Now let’s look at some more examples and some important **properties** of the expectation like linearity:

We continue with another important concept: **variance**. With this we can measure how much a random variable deviates from its mean.