Understanding bivariate and univariate data
From playlist Integrated Algebra Regents
(ML 7.10) Posterior distribution for univariate Gaussian (part 2)
Computing the posterior distribution for the mean of the univariate Gaussian, with a Gaussian prior (assuming known prior mean, and known variances). The posterior is Gaussian, showing that the Gaussian is a conjugate prior for the mean of a Gaussian.
From playlist Machine Learning
From playlist UNSW Medicine
What is a Unimodal Distribution?
Quick definition of a unimodal distribution and how it compares to a bimodal distribution and a multimodal distribution.
From playlist Probability Distributions
(ML 7.9) Posterior distribution for univariate Gaussian (part 1)
Computing the posterior distribution for the mean of the univariate Gaussian, with a Gaussian prior (assuming known prior mean, and known variances). The posterior is Gaussian, showing that the Gaussian is a conjugate prior for the mean of a Gaussian.
From playlist Machine Learning
(ML 6.2) MAP for univariate Gaussian mean
Computing the MAP for the mean of a univariate Gaussian with a Gaussian prior, assuming a known variance. A playlist of these Machine Learning videos is available here: http://www.youtube.com/view_play_list?p=D0F06AA0D2E8FFBA
From playlist Machine Learning
04 Spatial Data Analytics: Univariate Distributions
Basic univariate data analytics for subsurface modeling. Discussion on histogram, PDF and CDF.
From playlist Spatial Data Analytics and Modeling
UNSW Online courses: https://studyonline.unsw.edu.au/ Studying Online with UNSW is like studying at university in all respects other than you get to choose when you study and where you study.
From playlist UNSW Online
High-Performance Polynomial Algebra
The upcoming release of Mathematica includes significant performance improvements in polynomial algebra functions and in linear algebra for matrices of univariate polynomials. The release also includes functionality extensions in polynomial algebra over Zp. The improvements and extensions
From playlist Wolfram Technology Conference 2022
Neuroscience source separation 3a: Multivariate cross-frequency coupling
This is part three of a three-part lecture series I taught in a masters-level neuroscience course in fall of 2020 at the Donders Institute (the Netherlands). The lectures were all online in order to minimize the spread of the coronavirus. That's good for you, because now you can watch the
From playlist Neuroscience source separation (3-part lecture series)
(PP 6.10) Sum of independent Gaussians
A sum of independent (multivariate) Gaussians is (multivariate) Gaussian, with mean equal to the sum of the means, and covariance equal to the sum of the covariances.
From playlist Probability Theory
Andreas H. Hamel: From set-valued quantiles to risk measures: a set optimization approach to...
Abstract : Some questions in mathematics are not answered for quite some time, but just sidestepped. One of those questions is the following: What is the quantile of a multi-dimensional random variable? The "sidestepping" in this case produced so-called depth functions and depth regions, a
From playlist Probability and Statistics
Recorded: Spring 2015 Lecturer: Dr. Erin M. Buchanan Data Screening Video Demo - based on information from Tabachnick and Fidell (2012) and Field (2014). Please note: this video was recorded with Blackboard collaborate - the quality was very reduced because of using this software. Anothe
From playlist Advanced Statistics Videos
Types Of Data | Statistics & Probability | Maths | FuseSchool
CREDITS Animation & Design: Waldi Apollis Narration: Lucy Billings Script: Lucy Billings Hi, I’m Lucy and in this video, we are going to look at the different types of data that exist and how it can be classified. Starting with data collection... If data is collected by or for the compa
From playlist MATHS
Linear regression is used to compare sets or pairs of numerical data points. We use it to find a correlation between variables.
From playlist Learning medical statistics with python and Jupyter notebooks
Efficiently Learning Mixtures of Gaussians - Ankur Moitra
Efficiently Learning Mixtures of Gaussians Ankur Moitra Massachusetts Institute of Technology January 18, 2011 Given data drawn from a mixture of multivariate Gaussians, a basic problem is to accurately estimate the mixture parameters. We provide a polynomial-time algorithm for this proble
From playlist Mathematics
Learn what it takes to teach online and face-to-face students together at UNSW, and about the benefits a hybrid approach can offer. Find out more at unsw.to/hybridlearning
From playlist Hybrid Learning & Teaching
SPSS One-Way Between Subjects ANOVA Example
Recorded: Spring 2015 Lecturer: Dr. Erin M. Buchanan Note: This video has an unfortunate amount of background noise and will be replaced sometime in the future. Lecture materials and assignments available at statisticofdoom.com. https://statisticsofdoom.com/page/basic-statistics/ Used i
From playlist Learn and Use G*Power