Probability Theory Basics

1. Basic Concepts of Probability Theory

We will start our way of learning probability theory by considering some basic definitions and rules: what is a stochastic experiment and random event, what is independence and incompatibility of events in the context of probability theory, what is the probability and how can we calculate probabilities of different elementary events.

Stochastic Experiment and Random Event

Probability and It's Properties

Geometrical Probability

Challenge: Solving the Task Using Geometric Probability

Independence and Incompatibility of Random Events

Conditional Probability

2. Probability of Complex Events

In real-life tasks, we often have to deal with complex relationships and, as a result, calculate probabilities of several events or events that depend on each other. Let's consider how we can do this using probability theory.

Inclusion-Exclusion Principle

Challenge: Solving the Task Using Inclusion-Exclusion Principle

The Multiplication Rule of Probability

Law of Total Probability

Bayes' Theorem

Challenge: Solving the Task Using Bayes' Theorem

3. Commonly Used Discrete Distributions

To solve many real problems in probability theory, special models have been created that describe a particular situation. Let's consider some of the most used models that can be used to describe some discrete results of stochastic experiments.

Binomial Distribution

Challenge: Solving Task Using Binomial Distribution

Multinomial Distribution

Geometric Distribution

Poisson Distribution

Challenge: Solving Task Using Poisson Distribution

4. Commonly Used Continuous Distributions

What if the result of a stochastic experiment cannot be described by a discrete value? For this, models that work with continuous values are used. Consider the most popular of these models.

Continuous Uniform Distribution

Exponential Distribution

Challenge: Solving Task Using Exponential Distribution

Gaussian Distribution

Challenge: Solving Task Using Gaussian Distribution

5. Covariance and Correlation

Often we are faced with the task of checking the dependence of the results of different stochastic experiments on each other. Moreover, it is necessary not only to assess the presence of dependencies but also to somehow quantify the degree of dependencies. To solve these problems, we can use covariance and correlation.

What is Covariance?

What is Correlation?

Challenge: Solving the Task Using Correlation

Law of Total Probability

The law of total probability is a fundamental concept in probability theory. This law can be formulated as follows:

Let's provide some explanations:

We have split our space of elementary events into n different incompatible events;
We want to calculate the probability of some other event in this space of elementary events;
We can calculate P(A) using the formula described above.

This law is often used when a stochastic experiment can be divided into different stages, and each stage is stochastic too.

Example

Let's consider an example involving a manufacturing company that produces two types of products: Product 1 and Product 2.
The company produces 60% of Product 1 and 40% of Product 2.
The defect rate for Product 1 is 10%, while the defect rate for Product 2 is 5%. We want to calculate the probability of randomly selecting a defective product from the company's inventory.

In this example:

Event A: Selecting a defective product.
Partition events: H₁ = Selecting Product 1, H₂ = Selecting Product 2.
Now we can use the law of total probability to solve this task:

Все було зрозуміло?

Секція 2. Розділ 4

Зміст курсу

Probability Theory Basics

1. Basic Concepts of Probability Theory

Stochastic Experiment and Random Event Probability and It's Properties Geometrical Probability Challenge: Solving the Task Using Geometric Probability Independence and Incompatibility of Random Events Conditional Probability

2. Probability of Complex Events

Inclusion-Exclusion Principle Challenge: Solving the Task Using Inclusion-Exclusion Principle The Multiplication Rule of Probability Law of Total Probability Bayes' Theorem Challenge: Solving the Task Using Bayes' Theorem

3. Commonly Used Discrete Distributions

Binomial Distribution Challenge: Solving Task Using Binomial Distribution Multinomial Distribution Geometric Distribution Poisson Distribution Challenge: Solving Task Using Poisson Distribution

4. Commonly Used Continuous Distributions

Continuous Uniform Distribution Exponential Distribution Challenge: Solving Task Using Exponential Distribution Gaussian Distribution Challenge: Solving Task Using Gaussian Distribution

5. Covariance and Correlation

What is Covariance?What is Correlation?Challenge: Solving the Task Using Correlation

Probability Theory Basics

Law of Total Probability

The law of total probability is a fundamental concept in probability theory. This law can be formulated as follows:

Let's provide some explanations:

We have split our space of elementary events into n different incompatible events;
We want to calculate the probability of some other event in this space of elementary events;
We can calculate P(A) using the formula described above.

This law is often used when a stochastic experiment can be divided into different stages, and each stage is stochastic too.

Example

In this example:

Event A: Selecting a defective product.
Partition events: H₁ = Selecting Product 1, H₂ = Selecting Product 2.
Now we can use the law of total probability to solve this task:

Все було зрозуміло?

Секція 2. Розділ 4