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Probability Theory Update
course content

Contenido del Curso

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

bookBinomial 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 desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
¿Todo estuvo claro?

¿Cómo podemos mejorarlo?

¡Gracias por tus comentarios!

Sección 4. Capítulo 1
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bookBinomial 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 desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
¿Todo estuvo claro?

¿Cómo podemos mejorarlo?

¡Gracias por tus comentarios!

Sección 4. Capítulo 1
toggle bottom row

bookBinomial 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 desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
¿Todo estuvo claro?

¿Cómo podemos mejorarlo?

¡Gracias por tus comentarios!

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 desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
Sección 4. Capítulo 1
Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
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