geometric distribution

The geometric distribution is a probability distribution that models the number of trials needed to achieve the first success in a series of independent Bernoulli trials. It is characterized by a constant probability of success on each trial. This distribution is particularly useful in various fields such as finance, sports, and commerce, where it helps in understanding scenarios like the number of attempts required to win a game or make a sale. The geometric distribution is defined by its simplicity and ease of interpretation, making it a fundamental concept in statistics and probability theory.

Geometric Distribution Simply Explained

Towards Data Science

A simple description and uses of the Geometric distribution Continue reading on Towards Data Science

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…

Understanding The Hypergeometric Distribution

Towards Data Science

The binomial distribution is a well-known distribution in and outside of data science. However, have you heard about its less popular cousin the hypergeometric distribution? Well if not, this post…

Distributions

Think Bayes

In the previous chapter we used Bayes’s Theorem to solve a cookie problem; then we solved it again using a Bayes table. In this chapter, at the risk of testing your patience, we will solve it one mor...

A Gentle Introduction to Statistical Data Distributions

Machine Learning Mastery

Last Updated on August 8, 2019 A sample of data will form a distribution, and by far the most well-known distribution is the Gaussian distribution, often called the Normal distribution. The distributi...

Visualizing Beta Distribution and Bayesian Updating

Towards Data Science

Beta distribution is one of the more esoteric distributions compared to Bernoulli, Binomial and Geometric distributions. This post supplements intuitive understanding with visual learning.

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…...

Geometric Priors I

Towards Data Science

Geometric Deep Learning Groups, Representations, Invariance & Equivariance A series of blog posts that summarize the Geometric Deep Learning (GDL) Course, at AMMI program; African Master’s of Machine...

Stochastic Processes Simulation — Generalized Geometric Brownian Motion

Towards Data Science

Stochastic Processes Simulation — Generalized Geometric Brownian Motion Part 5 of the Stochastic Processes Simulation series. Simulate generalized Brownian motion in Python. Image by author. Alas, we...

The Gaussian Distribution explained

Towards Data Science

An example in MATLAB Continue reading on Towards Data Science

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 ...

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...