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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

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

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

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

Gaussian Distribution

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  