Exponential family distributions | Discrete distributions | Infinitely divisible probability distributions

Geometric distribution

In probability theory and statistics, the geometric distribution is either one of two discrete probability distributions: * The probability distribution of the number X of Bernoulli trials needed to get one success, supported on the set ; * The probability distribution of the number Y = X − 1 of failures before the first success, supported on the set . Which of these is called the geometric distribution is a matter of convention and convenience. These two different geometric distributions should not be confused with each other. Often, the name shifted geometric distribution is adopted for the former one (distribution of the number X); however, to avoid ambiguity, it is considered wise to indicate which is intended, by mentioning the support explicitly. The geometric distribution gives the probability that the first occurrence of success requires k independent trials, each with success probability p. If the probability of success on each trial is p, then the probability that the kth trial (out of finite trials) is the first success is for k = 1, 2, 3, 4, .... The above form of the geometric distribution is used for modeling the number of trials up to and including the first success. By contrast, the following form of the geometric distribution is used for modeling the number of failures until the first success: for k = 0, 1, 2, 3, .... In either case, the sequence of probabilities is a geometric sequence. For example, suppose an ordinary die is thrown repeatedly until the first time a "1" appears. The probability distribution of the number of times it is thrown is supported on the infinite set { 1, 2, 3, ... } and is a geometric distribution with p = 1/6. The geometric distribution is denoted by Geo(p) where 0 < p ≤ 1. (Wikipedia).

Geometric distribution
Video thumbnail

Geometric Distribution - Probability, Mean, Variance, & Standard Deviation

This statistics video tutorial explains how to calculate the probability of a geometric distribution function. It also explains how to calculate the mean, variance, and standard deviation. It contains plenty of example problems with the formulas needed to solve them. My Website: https:

From playlist Statistics

Video thumbnail

Determining the sum of a geometric sum when there is no sum

👉 Learn how to find the geometric sum of a series. A series is the sum of the terms of a sequence. A geometric series is the sum of the terms of a geometric sequence. The formula for the sum of n terms of a geometric sequence is given by Sn = a[(r^n - 1)/(r - 1)], where a is the first term

From playlist Series

Video thumbnail

Learn how to determine the sum of a geometric finite series

👉 Learn how to find the geometric sum of a series. A series is the sum of the terms of a sequence. A geometric series is the sum of the terms of a geometric sequence. The formula for the sum of n terms of a geometric sequence is given by Sn = a[(r^n - 1)/(r - 1)], where a is the first term

From playlist Series

Video thumbnail

How to determine the sum of an finite geometric series

👉 Learn how to find the geometric sum of a series. A series is the sum of the terms of a sequence. A geometric series is the sum of the terms of a geometric sequence. The formula for the sum of n terms of a geometric sequence is given by Sn = a[(r^n - 1)/(r - 1)], where a is the first term

From playlist Series

Video thumbnail

Learning to find the partial sum of a geometric series

👉 Learn how to write the sum from a geometric series. A series is the sum of the terms of a sequence. A geometric series is the sum of the terms of a geometric sequence. The formula for the sum of n terms of a geometric sequence is given by Sn = a[(r^n - 1)/(r - 1)], where a is the first t

From playlist Series

Video thumbnail

How to determine the sum of a infinite geometric series

👉 Learn how to find the geometric sum of a series. A series is the sum of the terms of a sequence. A geometric series is the sum of the terms of a geometric sequence. The formula for the sum of n terms of a geometric sequence is given by Sn = a[(r^n - 1)/(r - 1)], where a is the first term

From playlist Series

Video thumbnail

How to determine the infinite sum of a geometric series

👉 Learn how to find the geometric sum of a series. A series is the sum of the terms of a sequence. A geometric series is the sum of the terms of a geometric sequence. The formula for the sum of n terms of a geometric sequence is given by Sn = a[(r^n - 1)/(r - 1)], where a is the first term

From playlist Series

Video thumbnail

Determine the sum of a finite geometric sequence

👉 Learn how to find the geometric sum of a series. A series is the sum of the terms of a sequence. A geometric series is the sum of the terms of a geometric sequence. The formula for the sum of n terms of a geometric sequence is given by Sn = a[(r^n - 1)/(r - 1)], where a is the first term

From playlist Series

Video thumbnail

Expressing the sum using sum notation of a geometric series

👉 Learn how to write the sum from a geometric series. A series is the sum of the terms of a sequence. A geometric series is the sum of the terms of a geometric sequence. The formula for the sum of n terms of a geometric sequence is given by Sn = a[(r^n - 1)/(r - 1)], where a is the first t

From playlist Series

Video thumbnail

Geometric Setting & Distribution in Statistics

I introduce the Geometric Setting & Distribution in statistics and compare it to the Binomial Setting. This video includes setting up a PDF, examples of finding probabilities, and a non-example of a geometric setting. Find free review test, useful notes and more at http://www.mathplane.co

From playlist AP Statistics

Video thumbnail

OCR MEI Statistics Minor H: Geometric Distribution: 02 Binomial vs Geometric

https://www.buymeacoffee.com/TLMaths Navigate all of my videos at https://sites.google.com/site/tlmaths314/ Like my Facebook Page: https://www.facebook.com/TLMaths-1943955188961592/ to keep updated Follow me on Instagram here: https://www.instagram.com/tlmaths/ Many, MANY thanks to Dea

From playlist OCR MEI Statistics Minor H: Geometric Distribution

Video thumbnail

Lognormal value at risk (VaR, FRM T5-01)

Welcome to the first video in this new playlist that is devoted to Topic 5 in the FRM. Topic 5, Market Risk, is the first topic in Part 2. We will start here by comparing normal to lognormal VaR and, specifically, we are going to generalize to absolute VaR. Absolute VaR generalizes the rel

From playlist Market Risk (FRM Topic 5)

Video thumbnail

Wein-Wei Li: Full stable trace formula for the group Mp(2n)

CIRM VIRTUAL EVENT Recorded during the meeting "Relative Aspects of the Langlands Program, L-Functions and Beyond Endoscopy the May 24, 2021 by the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Luca Récanzone Find this video and other talks given by w

From playlist Virtual Conference

Video thumbnail

Parametric Probability Distribution Fitted to Data with Bayes's Theorem

James Rock explains how he's using Bayes's Theorem to fit data to a parametric distribution with Mathematica in this talk from the Wolfram Technology Conference. For more information about Mathematica, please visit: http://www.wolfram.com/mathematica

From playlist Wolfram Technology Conference 2012

Video thumbnail

Ch9Pr18: Probability Distributions

A gentle introduction to probability distributions by looking at the uniform, binomial, geometric and Poisson distributions. This is Chapter 9 Problem 18 from the MATH1231/1241 Algebra notes. Presented by Thomas Britz from UNSW.

From playlist Mathematics 1B (Algebra)

Video thumbnail

Geometric Distributions and The Birthday Paradox: Crash Course Statistics #16

Geometric probabilities, and probabilities in general, allow us to guess how long we'll have to wait for something to happen. Today, we'll discuss how they can be used to figure out how many Bertie Bott's Every Flavour Beans you could eat before getting the dreaded vomit flavored bean, and

From playlist Statistics

Video thumbnail

Python for Data Analysis: Probability Distributions

This video covers the basics of working with probability distributions in Python, including the uniform, normal, binomial, geometric, exponential and Poisson distributions. It also includes a discussion of random number generation and setting the random seed. Subscribe: ► https://www.yout

From playlist Python for Data Analysis

Video thumbnail

Elisabeth Gassiat - Manifold Learning with Noisy Data

It is a common idea that high dimensional data (or features) may lie on low dimensional support making learning easier. In this talk, I will present a very general set-up in which it is possible to recover low dimensional non-linear structures with noisy data, the noise being totally unkno

From playlist 8th edition of the Statistics & Computer Science Day for Data Science in Paris-Saclay, 9 March 2023

Video thumbnail

Geometric Algebra in 3D - The Vector-Bivector Product (Part 2)

In this second part, we'll investigate the algebraic properties and geometric meaning of this extended dot and wedge product. We'll see that vectors in the plane of a bivector anticommute with the bivector under the geometric product and vectors orthogonal to the plane of a bivector will

From playlist Math

Video thumbnail

How to determine the sum of an infinite geometric series

👉 Learn how to find the geometric sum of a series. A series is the sum of the terms of a sequence. A geometric series is the sum of the terms of a geometric sequence. The formula for the sum of n terms of a geometric sequence is given by Sn = a[(r^n - 1)/(r - 1)], where a is the first term

From playlist Series

Related pages

Beta distribution | Hypergeometric distribution | Conjugate prior | Statistics | Coupon collector's problem | Indecomposable distribution | Sample mean | Exponential distribution | Floor and ceiling functions | Infinite divisibility (probability) | Method of moments (statistics) | Compound Poisson distribution | Bernoulli trial | Probability-generating function | Poisson distribution | Polylogarithm | Variance | Maximum likelihood estimation | R (programming language) | Maximum entropy probability distribution | Power series | Real number | Negative binomial distribution | Memorylessness | Compact space | Random variable | Pseudorandom number generator | Dice | Expected value | Probability theory | Numeral system | Uniform convergence | Bayesian inference | Prefix code | Cumulant