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Probability Theory Update
course content

Course Content

Probability Theory Update

Probability Theory Update

1. Probability Basics
Set TheoryProbability ExperimentStatistical Independence
2. Statistical Dependence
Dependent EventsMutually Exclusive EventsAddition Rule for Mutually Exclusive EventsAddition Rule for Non-Mutually Exclusive EventsMultiplication Rule for Independent EventsMultiplication Rule for Dependent EventsConditional probabilityBayes Rule
3. Learn Crucial Terms
Random VariableDiscrete vs Continuous Random VariableExpected ValueThe Law of Large NumbersCMT
4. Probability Functions
Binomial DistributionProbability Mass Function (PMF) 1/2Probability Mass Function (PMF) 2/2Cumulative Distribution Function (CDF) 1/2Cumulative Distribution Function (CDF) 2/2Probability Density Function (PDF) 1/2Probability Density Function (PDF) 2/2
5. Distributions
Poisson Distribution 1/3Poisson Distribution 2/3Poisson Distribution 3/3Standard Normal Distribution (Gaussian distribution) 1/2

Binomial Distribution

What is it?

This distribution with parameters n (number of trials, each one has only two outcomes: success or failure) and p (probability that event happened) is the discrete probability distribution.

Key characteristics:

  • Number of trials is finite.
  • The trials sre independent.
  • Probability of success is constant for each trial.
  • Each trial can result only in success or failure.

Examples:

The most clear example is tossing a coin.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Section 4. Chapter 1
toggle bottom row

Binomial Distribution

What is it?

This distribution with parameters n (number of trials, each one has only two outcomes: success or failure) and p (probability that event happened) is the discrete probability distribution.

Key characteristics:

  • Number of trials is finite.
  • The trials sre independent.
  • Probability of success is constant for each trial.
  • Each trial can result only in success or failure.

Examples:

The most clear example is tossing a coin.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Section 4. Chapter 1
toggle bottom row

Binomial Distribution

What is it?

This distribution with parameters n (number of trials, each one has only two outcomes: success or failure) and p (probability that event happened) is the discrete probability distribution.

Key characteristics:

  • Number of trials is finite.
  • The trials sre independent.
  • Probability of success is constant for each trial.
  • Each trial can result only in success or failure.

Examples:

The most clear example is tossing a coin.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

What is it?

This distribution with parameters n (number of trials, each one has only two outcomes: success or failure) and p (probability that event happened) is the discrete probability distribution.

Key characteristics:

  • Number of trials is finite.
  • The trials sre independent.
  • Probability of success is constant for each trial.
  • Each trial can result only in success or failure.

Examples:

The most clear example is tossing a coin.

Switch to desktop for real-world practiceContinue from where you are using one of the options below
Section 4. Chapter 1
Switch to desktop for real-world practiceContinue from where you are using one of the options below
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