Experiment (probability theory)

Sample space

In probability theory, the sample space (also called sample description space, possibility space, or outcome space) of an experiment or random trial is the set of all possible outcomes or results of that experiment. A sample space is usually denoted using set notation, and the possible ordered outcomes, or sample points, are listed as elements in the set. It is common to refer to a sample space by the labels S, Ω, or U (for "universal set"). The elements of a sample space may be numbers, words, letters, or symbols. They can also be finite, countably infinite, or uncountably infinite. A subset of the sample space is an event, denoted by . If the outcome of an experiment is included in , then event has occurred. For example, if the experiment is tossing a single coin, the sample space is the set , where the outcome means that the coin is heads and the outcome means that the coin is tails. The possible events are , , and . For tossing two coins, the sample space is , where the outcome is if both coins are heads, if the first coin is heads and the second is tails, if the first coin is tails and the second is heads, and if both coins are tails. The event that at least one of the coins is heads is given by . For tossing a single six-sided die one time, where the result of interest is the number of pips facing up, the sample space is . A well-defined, non-empty sample space is one of three components in a probabilistic model (a probability space). The other two basic elements are: a well-defined set of possible events (an event space), which is typically the power set of if is discrete or a σ-algebra on if it is continuous, and a probability assigned to each event (a probability measure function). A sample space can be represented visually by a rectangle, with the outcomes of the sample space denoted by points within the rectangle. The events may be represented by ovals, where the points enclosed within the oval make up the event. (Wikipedia).

Sample space
Video thumbnail

Probability & Statistics (3 of 62) Definition of Sample Spaces & Factorials

Visit http://ilectureonline.com for more math and science lectures! In this video I will define what are sample spaces and factorials. Next video in series: http://youtu.be/EOk25Tb-1bM

From playlist Michel van Biezen: PROBABILITY & STATISTICS 1 BASICS

Video thumbnail

Sampling Frame Definition, Example

What is a sampling frame? Examples of different types of sampling frames. Check out my e-book, Sampling in Statistics, which covers everything you need to know to find samples with more than 20 different techniques: https://prof-essa.creator-spring.com/listing/sampling-in-statistics

From playlist Sampling

Video thumbnail

Sample Space of Combined Events

"List a sample space for one event or combined events."

From playlist Data Handling: Probability

Video thumbnail

Sample Spaces | Statistics & Probability | Maths | FuseSchool

Sample Spaces | Statistics & Probability | Maths | FuseSchool In this video we are going to look at a way of visualising outcomes from a set of probability events.A sample space shows us all the possible outcomes of one or more events. A sample space diagram is a useful way to look at mul

From playlist MATHS

Video thumbnail

Surveys & questionnaires (2)

Powered by https://www.numerise.com/ Surveys & questionnaires (2)

From playlist Collecting data

Video thumbnail

4.1.5 Sample Spaces: Video

MIT 6.042J Mathematics for Computer Science, Spring 2015 View the complete course: http://ocw.mit.edu/6-042JS15 Instructor: Albert R. Meyer License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu

From playlist MIT 6.042J Mathematics for Computer Science, Spring 2015

Video thumbnail

Intro to Sample Proportions

An overview and introduction to understanding sampling distributions of proportions [sample proportions] and how to calculate them

From playlist Unit 7 Probability C: Sampling Distributions & Simulation

Video thumbnail

Introduction of Sample Proportions

A set of notes to help you understand the Binomial Setting and how to set up binomial proportions. Find free review test, useful notes and more at http://www.mathplane.com If you'd like to make a donation to support my efforts look for the "Tip the Teacher" button on my channel's homepage

From playlist AP Statistics

Video thumbnail

Frank Noé: "Connection between Statistics and Machine Learning"

Machine Learning for Physics and the Physics of Learning Tutorials 2019 "Connection between Statistics and Machine Learning" Frank Noé, Freie Universität Berlin Institute for Pure and Applied Mathematics, UCLA September 10, 2019 For more information: http://www.ipam.ucla.edu/programs/wo

From playlist Machine Learning for Physics and the Physics of Learning 2019

Video thumbnail

Texture Sampling #1: Points & Borders

Texture Sampling is fundamental to all grahics these days, but I thought I'd have a bit of an exploration of what exactly goes on trying to emulate what we have come to expect of GPUs. In this video, I establish a little framework, and then create a texture sampling class to demonstrate va

From playlist Interesting Programming

Video thumbnail

Excel Statistical Analysis 16: Introduction to Probability. Power Query & Pivot Table Example too

Download Excel File: https://excelisfun.net/files/Ch04-ESA.xlsm pdf notes: https://excelisfun.net/files/Ch04-ESA.pdf Learn about the basics of probability: Topics: 1. (00:00) Introduction 2. (00:58) Examples of Probability 3. (02:46) What is Probability? Define Probability. 4. (05:14) Type

From playlist Excel Statistical Analysis for Business Class Playlist of Videos from excelisfun

Video thumbnail

Phiala Shanahan: "Machine learning for lattice field theory"

Machine Learning for Physics and the Physics of Learning 2019 Workshop I: From Passive to Active: Generative and Reinforcement Learning with Physics "Machine learning for lattice field theory" Phiala Shanahan, Massachusetts Institute of Technology (MIT) Abstract: I will discuss opportuni

From playlist Machine Learning for Physics and the Physics of Learning 2019

Video thumbnail

Frank Noé: "Deep Generative Learning for Physics Many-Body Systems"

Machine Learning for Physics and the Physics of Learning 2019 Workshop I: From Passive to Active: Generative and Reinforcement Learning with Physics "Deep Generative Learning for Physics Many-Body Systems" Frank Noé, Freie Universität Berlin Institute for Pure and Applied Mathematics, UC

From playlist Machine Learning for Physics and the Physics of Learning 2019

Video thumbnail

Deep Learning Lecture 11.1 - Variational Autoencoders

Introduction to directed Generative Networks Transformation of Random Variables Variational Autoencoders

From playlist Deep Learning Lecture

Video thumbnail

Yohai Reani (9/21/22): Persistent Cycle Registration and Topological Bootstrap

In this talk we will present a novel approach for comparing the persistent homology representations of two spaces (filtrations). Commonly used comparison methods are based on numerical summaries such as persistence diagrams and persistence landscapes, along with suitable metrics (e.g. Wass

From playlist AATRN 2022

Video thumbnail

OpenGL - SSAO (screen space ambient occlusion)

Code samples derived from work by Joey de Vries, @joeydevries, author of https://learnopengl.com/ All code samples, unless explicitly stated otherwise, are licensed under the terms of the CC BY-NC 4.0 license as published by Creative Commons, either version 4 of the License, or (at your o

From playlist OpenGL

Video thumbnail

Dosik Hwang: "Deep Learning-based MR Image Reconstruction and Contrast Conversion"

Deep Learning and Medical Applications 2020 "Deep Learning-based MR Image Reconstruction and Contrast Conversion" Dosik Hwang, Yonsei University Abstract: In this talk, I will present recent works on magnetic resonance image reconstruction using deep learning techniques applied to both k

From playlist Deep Learning and Medical Applications 2020

Video thumbnail

1. Probability Models and Axioms

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

Video thumbnail

Sampling (2 of 5: Introduction to Random Samples and Spreadsheets)

More resources available at www.misterwootube.com

From playlist Data Analysis

Related pages

Countable set | Finite set | Statistics | Outcome (probability) | Simple random sample | Probability space | Probability | Statistical population | Parameter space | Uncountable set | Event (probability theory) | Discrete uniform distribution | Pip (counting) | Bias of an estimator | Element (mathematics) | Universe (mathematics) | Set (mathematics) | Cartesian product | Probability measure | Dice | Σ-algebra | Probability theory | Measure (mathematics) | Experiment (probability theory) | Power set | Space (mathematics)