discrete distributions

Discrete distributions are fundamental concepts in probability and statistics that describe the likelihood of outcomes for discrete random variables. These distributions are characterized by distinct, separate values, making them essential for modeling scenarios where outcomes are countable, such as coin flips or dice rolls. Common examples include the Bernoulli distribution, which represents a single binary outcome, and the Binomial distribution, which models a sequence of binary outcomes. Understanding discrete distributions is crucial for various applications, particularly in machine learning, where they inform classification tasks and performance evaluation metrics. Their mathematical properties and visual representations aid in grasping their significance in data analysis.

The Bernoulli and Binomial Distributions

Analytics Vidhya

The probability for a discrete random variable can be summarized with a discrete probability distribution. A discrete random variable is a random variable that can have one of a finite set of…

An Introduction to Discrete Distribution Function

Python in Plain English

Binomial Distribution: In probability theory and statistics, the binomial distribution is the discrete probability distribution that gives only two possible results in an experiment, either Success…

The Discrete ‘Movie Rating’ Distribution

Towards AI

Distributions are pre-defined math functions that describe a real-world pattern (this is not the formal definition, however is a very intuitive understanding of why you would ever study distributions…...

The 5 discrete distributions every Data Scientist should know

Towards Data Science

Now coming from a non-statistical background, distributions always come across as something mystical to me. This post is about some of the most used discrete distributions that you need to know along…...

Discrete Probability Distributions with R

Towards AI

In simpler terms, the probability distributions describe the random process (any phenomenon) in terms of probabilities. A random event is a random process to which we can never find the exact value…

Discrete Probability Distributions for Machine Learning

Machine Learning Mastery

Last Updated on October 6, 2020 The probability for a discrete random variable can be summarized with a discrete probability distribution. Discrete probability distributions are used in machine learni...

Univariate Discrete Distributions: An Easy-to-Understand Explanation

Towards Data Science

Do you know this feeling? You want to learn something new, but you don’t know where to start. This is how we felt when we wanted to understand distributions mathematically. Yes, our professor explaine...

Statistical Distributions

Towards Data Science

A probability distribution is a mathematical function that provides the probabilities of the occurrence of various possible outcomes in an experiment. Probability distributions are used to define…

Seven Must-Know Statistical Distributions and Their Simulations for Data Science

Towards Data Science

A statistical distribution is a parameterized mathematical function that gives the probabilities of different outcomes for a random variable. There are discrete and continuous distributions depending…...

Distributions

Dive intro Deep Learning Book

Now that we have learned how to work with probability in both the discrete and the continuous setting, let’s get to know some of the common distributions encountered. Depending on the area of machine ...

7 Types of Discrete Probability Distributions and Their Applications in R

Analytics Vidhya

7 types of Discrete Probability Distributions clearly explained in 10 minutes. (With code in R)!

Distributions

Elements of Data Science

Click here to run this notebook on Colab or click here to download it . In this chapter we’ll see three ways to describe a set of values: A probability mass function (PMF), which represents a set of ...