Theory of probability distributions
In probability theory and statistics, the marginal distribution of a subset of a collection of random variables is the probability distribution of the variables contained in the subset. It gives the probabilities of various values of the variables in the subset without reference to the values of the other variables. This contrasts with a conditional distribution, which gives the probabilities contingent upon the values of the other variables. Marginal variables are those variables in the subset of variables being retained. These concepts are "marginal" because they can be found by summing values in a table along rows or columns, and writing the sum in the margins of the table. The distribution of the marginal variables (the marginal distribution) is obtained by marginalizing (that is, focusing on the sums in the margin) over the distribution of the variables being discarded, and the discarded variables are said to have been marginalized out. The context here is that the theoretical studies being undertaken, or the data analysis being done, involves a wider set of random variables but that attention is being limited to a reduced number of those variables. In many applications, an analysis may start with a given collection of random variables, then first extend the set by defining new ones (such as the sum of the original random variables) and finally reduce the number by placing interest in the marginal distribution of a subset (such as the sum). Several different analyses may be done, each treating a different subset of variables as the marginal variables. (Wikipedia).
The Normal Distribution (1 of 3: Introductory definition)
More resources available at www.misterwootube.com
From playlist The Normal Distribution
What is a Sampling Distribution?
Intro to sampling distributions. What is a sampling distribution? What is the mean of the sampling distribution of the mean? 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.creat
From playlist Probability Distributions
(PP 6.8) Marginal distributions of a Gaussian
For any subset of the coordinates of a multivariate Gaussian, the marginal distribution is multivariate Gaussian.
From playlist Probability Theory
In this video we cover the idea of marginal cost. This is simply the derivative of the cost function. We can roughly define marginal cost as the cost of producing one additional item. For more videos please visit http://www.mysecretmathtutor.com
From playlist Calculus
We use the Normal Distribution app on ArtofSat.com to show how to find probabilities and percentiles under the normal distribution. We also use the app to explain how the two parameters mu (the mean) and sigma (the standard deviation) determine the shape of the distribution.
From playlist Chapter 6: Distributions
Sampling Distribution of the PROPORTION: Friends of P (12-2)
The sampling distribution of the proportion is the probability distribution of all possible values of the sample proportions. It is analogous to the Distribution of Sample Means. When the sample size is large enough, the sampling distribution of the proportion can be approximated by a norm
From playlist Sampling Distributions in Statistics (WK 12 - QBA 237)
Determining values of a variable at a particular percentile in a normal distribution
From playlist Unit 2: Normal Distributions
Uniform Probability Distribution Examples
Overview and definition of a uniform probability distribution. Worked examples of how to find probabilities.
From playlist Probability Distributions
2d Data Analytics Reboot: Joint, Marginal, Conditional Probability
A supplemental lecture with discussion on joint, conditional and marginal, probability and distributions. Follow along with the demonstration workflow in Excel: o. Marginal, conditional and joint probability calculation: https://github.com/GeostatsGuy/ExcelNumericalDemos/blob/master/Marg
From playlist Data Analytics and Geostatistics
From playlist COMP0168 (2020/21)
Excel Statistical Analysis 41: Confidence Interval for t Distribution, use when Sigma NOT Known
Download Excel File: https://excelisfun.net/files/Ch08-ESA.xlsm PDF notes file: https://excelisfun.net/files/Ch08-ESA.pdf Learn about how to create Confidence Interval to estimate a population Mean when Sigma (Population Standard Deviation) is NOT Known using the t Distribution and the Ex
From playlist Excel Statistical Analysis for Business Class Playlist of Videos from excelisfun
DeepMind x UCL | Deep Learning Lectures | 11/12 | Modern Latent Variable Models
This lecture, by DeepMind Research Scientist Andriy Mnih, explores latent variable models, a powerful and flexible framework for generative modelling. After introducing this framework along with the concept of inference, which is central to it, Andriy focuses on two types of modern latent
From playlist Learning resources
Excel 2013 Statistical Analysis #50: t Distribution Confidence Intervals Sigma NOT Known 3 Examples
Download files (which file shown at begin of video): https://people.highline.edu/mgirvin/AllClasses/210Excel2013/Ch08/Ch08.htm Topics in this video: 1. (00:12) What is the t Distribution and when do we have to use it? Create Confidence Intervals when Population Standard Deviation is not kn
From playlist Excel for Statistical Analysis in Business & Economics Free Course at YouTube (75 Videos)
Marginal and conditional distributions | Analyzing categorical data | AP Statistics | Khan Academy
Keep going! Check out the next lesson and practice what you’re learning: https://www.khanacademy.org/math/ap-statistics/analyzing-categorical-ap/distributions-two-way-tables/e/identifying-marginal-conditional-distributions Marginal and conditional distributions from a two-way table (or jo
From playlist Analyzing categorical data | AP Statistics | Khan Academy
Excel 2010 Statistics 75 Confidence Intervals Sigma NOT Known T.INV, CONFIDENCE.T & Analysis Add-in
Download Excel File: https://people.highline.edu/mgirvin/AllClasses/210Excel2010/Content/Ch08/Busn210ch08.xlsm Download pdf file: https://people.highline.edu/mgirvin/AllClasses/210Excel2010/Content/Ch08/Ch08pdf-Busn210.pdf Two Examples for building Confidence Intervals Sigma NOT Known: 1.
From playlist Excel 2010 Videos
Sylvia Biscoveanu - Power Spectral Density Uncertainty and Gravitational-Wave Parameter Estimation
Recorded 19 November 2021. Sylvia Biscoveanu of the Massachusetts Institute of Technology presents "The Effect of Power Spectral Density Uncertainty on Gravitational-Wave Parameter Estimation" at IPAM's Workshop III: Source inference and parameter estimation in Gravitational Wave Astronomy
From playlist Workshop: Source inference and parameter estimation in Gravitational Wave Astronomy
Normal Distribution and Empirical Rule With Examples Lesson
This video provides a lesson on the standard normal distribution and the Empirical Rule. http://mathispower4u.com
From playlist The Normal Distribution
Chapter 7.4: Estimating a Population Mean (sigma unknown)
Chapter 7.4 from "Introduction to Statistics, Think & Do" by Scott Stevens (http://www.StevensStats.com) Textbook from Publisher, $29.95 print, $9.95 PDF http://www.centerofmathematics.com/wwcomstore/index.php/thinkdov4-1.html Textbook from Amazon: https://amzn.to/2zJRCjL
From playlist Statistics Lecture Videos