In probability theory and statistics, the geometric Poisson distribution (also called the Pólya–Aeppli distribution) is used for describing objects that come in clusters, where the number of clusters follows a Poisson distribution and the number of objects within a cluster follows a geometric distribution. It is a particular case of the compound Poisson distribution. The probability mass function of a random variable N distributed according to the geometric Poisson distribution is given by where λ is the parameter of the underlying Poisson distribution and θ is the parameter of the geometric distribution. The distribution was described by George Pólya in 1930. Pólya credited his student Alfred Aeppli's 1924 dissertation as the original source. It was called the geometric Poisson distribution by Sherbrooke in 1968, who gave probability tables with a precision of four decimal places. The geometric Poisson distribution has been used to describe systems modelled by a Markov model, such as biological processes or traffic accidents. (Wikipedia).
Statistics: Intro to the Poisson Distribution and Probabilities on the TI-84
This video defines a Poisson distribution and then shows how to find Poisson distribution probabilities on the TI-84.
From playlist Geometric Probability Distribution
Definition of a Poisson distribution and a solved example of the formula. 00:00 What is a Poisson distribution? 02:39 Poisson distribution formula 03:10 Solved example 04:22 Poisson distribution vs. binomial distribution
From playlist Probability Distributions
Poisson Distribution Probability with Formula: P(x equals k)
This video explains how to determine a Poisson distribution probability by hand using a formula. http://mathispower4u.com
From playlist Geometric Probability Distribution
Short Introduction to the Poisson Distribution
Please Subscribe here, thank you!!! https://goo.gl/JQ8Nys Short Introduction to the Poisson Distribution
From playlist Statistics
Statistics - 5.3 The Poisson Distribution
The Poisson distribution is used when we know a mean number of successes to expect in a given interval. We will learn what values we need to know and how to calculate the results for probabilities of exactly one value or for cumulative values. Power Point: https://bellevueuniversity-my
From playlist Applied Statistics (Entire Course)
The Poisson is a classic distribution used in operational risk. It often fits (describes) random variables over time intervals. For example, it might try to characterize the number of low severity, high frequency (HFLS) loss events over a month or a year. It is a discrete function that con
From playlist Statistics: Distributions
Poisson Distribution EXPLAINED!
http://www.zstatistics.com/videos/ 0:25 Quick rundown 2:15 Assumptions underlying the Poisson distribution 3:08 Probability Mass Function calculation 5:14 Cumulative Distribution Function calculation 6:29 Visualisation of the Poisson distribution 7:25 Practice QUESTION!
From playlist Distributions (10 videos)
Giovanni Peccati: Some applications of variational techniques in stochastic geometry I
Some variance estimates on the Poisson space, Part I I will introduce some basic tools of stochastic analysis on the Poisson space, and describe how they can be used to develop variational inequalities for assessing the magnitude of variances of geometric quantities. Particular attention
From playlist Winter School on the Interplay between High-Dimensional Geometry and Probability
MIT 6.041 Probabilistic Systems Analysis and Applied Probability, Fall 2010 View the complete course: http://ocw.mit.edu/6-041F10 Instructor: John Tsitsiklis License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
From playlist MIT 6.041SC Probabilistic Systems Analysis and Applied Probability, Fall 2013
The Poisson boundary: a qualitative theory by Vadim Kaimanovich
Program Probabilistic Methods in Negative Curvature ORGANIZERS: Riddhipratim Basu, Anish Ghosh and Mahan Mj DATE: 11 March 2019 to 22 March 2019 VENUE: Madhava Lecture Hall, ICTS, Bangalore The focal area of the program lies at the juncture of three areas: Probability theory o
From playlist Probabilistic Methods in Negative Curvature - 2019
The Poisson boundary: a qualitative theory (Lecture 3) by Vadim Kaimanovich
Program Probabilistic Methods in Negative Curvature ORGANIZERS: Riddhipratim Basu, Anish Ghosh and Mahan Mj DATE: 11 March 2019 to 22 March 2019 VENUE: Madhava Lecture Hall, ICTS, Bangalore The focal area of the program lies at the juncture of three areas: Probability theory o
From playlist Probabilistic Methods in Negative Curvature - 2019
Lec.2E: Poisson Distribution (With Example)
Lecture with Per B. Brockhoff. Chapters: 00:00 - Example 3; 02:30 - Definition;
From playlist DTU: Introduction to Statistics | CosmoLearning.org
Connecting Random Connection Models by Srikanth K Iyer
PROGRAM: ADVANCES IN APPLIED PROBABILITY ORGANIZERS: Vivek Borkar, Sandeep Juneja, Kavita Ramanan, Devavrat Shah, and Piyush Srivastava DATE & TIME: 05 August 2019 to 17 August 2019 VENUE: Ramanujan Lecture Hall, ICTS Bangalore Applied probability has seen a revolutionary growth in resear
From playlist Advances in Applied Probability 2019
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
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)
Poisson Distribution Probability with Formula: P(x less than or equal to k)
This video explains how to determine a Poisson distribution probability by hand using a formula. http://mathispower4u.com
From playlist Geometric Probability Distribution
03 03 Poisson regression, part 1 of 2
From playlist Coursera Regression V2
S23.2 Poisson Arrivals During an Exponential Interval
MIT RES.6-012 Introduction to Probability, Spring 2018 View the complete course: https://ocw.mit.edu/RES-6-012S18 Instructor: John Tsitsiklis License: Creative Commons BY-NC-SA More information at https://ocw.mit.edu/terms More courses at https://ocw.mit.edu
From playlist MIT RES.6-012 Introduction to Probability, Spring 2018
Introduction to Poisson Distribution - Probability & Statistics
This statistics video tutorial provides a basic introduction into the poisson distribution. It explains how to identify the mean with a changing time interval in order to calculate the probability of an event occurring. My Website: https://www.video-tutor.net Patreon Donations: https:/
From playlist Statistics
Discrete Probability Distributions (Binomial, Poisson, Hypergeometric) in Business Statistics (WK 9)
Description Building on probability, we now discover random variables and probability distributions, both of which are models for a population. We focus this week on discrete probability distributions, beginning with a simple table of random variables and probabilities. We then use mathema
From playlist Basic Business Statistics (QBA 237 - Missouri State University)