Using Variables in Science – The Foundations of Statistical Analysis and Scientific Testing (1-5)
Continuing our discussion about variables, you will learn how variables are used in science. Specifically, when we do statistics, we need independent and dependent variables. Independent variables are often categorical (groups) and dependent variables are typically measured on a scale. You
From playlist WK1 Numbers and Variables - Online Statistics for the Flipped Classroom
(PP 3.4) Random Variables with Densities
(0:00) Probability density function (PDF). (3:20) Indicator functions. (5:00) Examples of random variables with densities: Uniform, Exponential, Beta, Normal/Gaussian. A playlist of the Probability Primer series is available here: http://www.youtube.com/view_play_list?p=17567A1A3F5
From playlist Probability Theory
Linear regression (5): Bias and variance
Inductive bias; variance; relationship to over- & under-fitting
From playlist cs273a
How to Determine if Functions are Linearly Independent or Dependent using the Definition
How to Determine if Functions are Linearly Independent or Dependent using the Definition If you enjoyed this video please consider liking, sharing, and subscribing. You can also help support my channel by becoming a member https://www.youtube.com/channel/UCr7lmzIk63PZnBw3bezl-Mg/join Th
From playlist Zill DE 4.1 Preliminary Theory - Linear Equations
Prob & Stats - Random Variable & Prob Distribution (1 of 53) Random Variable
Visit http://ilectureonline.com for more math and science lectures! In this video I will define and gives an example of what is a random variable. Next video in series: http://youtu.be/aEB07VIIfKs
From playlist iLecturesOnline: Probability & Stats 2: Random Variable & Probability Distribution
Prob & Stats - Random Variable & Prob Distribution (4 of 53) Types of Random Variable
Visit http://ilectureonline.com for more math and science lectures! In this video I will define 2 types of random variables (discrete and continuous variables). Next video in series: http://youtu.be/mtt3h54aSkk
From playlist iLecturesOnline: Probability & Stats 2: Random Variable & Probability Distribution
This video provides a lesson on dependent function and verifying given functions are linear dependent. Site: http://mathispower4u.com
From playlist Second Order Differential Equations
Statistics: Ch 5 Discrete Random Variable (1 of 27) What is a Random Variable?
Visit http://ilectureonline.com for more math and science lectures! To donate: http://www.ilectureonline.com/donate https://www.patreon.com/user?u=3236071 We will learn a random variable is a variable which represents the outcome of a trial, an experiment, or an event. It is a specific n
From playlist STATISTICS CH 5 DISCRETE RANDOM VARIABLE
Andrew Ahn (Columbia) -- Airy edge fluctuations in random matrix sums
In this talk, we discuss a novel integrable probability approach to access edge fluctuations in sums of unitarily invariant Hermitian matrices. We focus on a particular regime where the number of summands is large (but fixed) under which the Airy point process appears. The approach is base
From playlist Columbia Probability Seminar
Homogenization and Correctors for Linear Stochastic Equations in.... by Mogtaba A. Y. Mohammed
DISCUSSION MEETING Multi-Scale Analysis: Thematic Lectures and Meeting (MATHLEC-2021, ONLINE) ORGANIZERS: Patrizia Donato (University of Rouen Normandie, France), Antonio Gaudiello (Università degli Studi di Napoli Federico II, Italy), Editha Jose (University of the Philippines Los Baño
From playlist Multi-scale Analysis: Thematic Lectures And Meeting (MATHLEC-2021) (ONLINE)
Spectra of Adjacency and Laplacian Matrices of ... by Arijit Chakrabarty
PROGRAM :UNIVERSALITY IN RANDOM STRUCTURES: INTERFACES, MATRICES, SANDPILES ORGANIZERS :Arvind Ayyer, Riddhipratim Basu and Manjunath Krishnapur DATE & TIME :14 January 2019 to 08 February 2019 VENUE :Madhava Lecture Hall, ICTS, Bangalore The primary focus of this program will be on the
From playlist Universality in random structures: Interfaces, Matrices, Sandpiles - 2019
Michael Damron (Georgia Tech) -- Critical first-passage percolation in two dimensions
In 2d first-passage percolation (FPP), we place nonnegative i.i.d. weights (t_e) on the edges of Z^2 and study the induced weighted graph pseudometric T = T(x,y). If we denote by p = P(t_e = 0), then there is a transition in the large-scale behavior of the model as p varies from 0 to 1. Wh
From playlist Columbia Probability Seminar
From order to chaos - Pisa, April, 11 - 2018
Centro di Ricerca Matematica Ennio De Giorgi http://crm.sns.it/event/419/ FROM ORDER TO CHAOS - Pisa 2018 Funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement N°647133) and partially supported by GNAMPA-I
From playlist Centro di Ricerca Matematica Ennio De Giorgi
OCR MEI Statistics Minor B: Linear Regression: 03 Random on Non-Random & Random on Random
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 B: Linear Regression
Alexey Bufetov: Representations of classical Lie groups: two growth regimes
Asymptotic representation theory deals with representations of groups of growing size. For classical Lie groups there are two distinguished regimes of growth. One of them is related to representations of infinite-dimensional groups, and the other appears in combinatorial and probabilistic
From playlist Probability and Statistics
Welcome to Quantitative Risk Management (QRM). In Lesson 6 we start discussing Time Series (TS) analysis, which we will later combine with EVT. We will answer the following questions: What is a TS? What types of TS can we model? What does stationarity mean? What are the main causes of non
From playlist Quantitative Risk Management
Limiting Laws in Some Subsequences Problems by Christian Houdré
PROGRAM : FIRST-PASSAGE PERCOLATION AND RELATED MODELS (HYBRID) ORGANIZERS : Riddhipratim Basu (ICTS-TIFR, India), Jack Hanson (City University of New York, US) and Arjun Krishnan (University of Rochester, US) DATE : 11 July 2022 to 29 July 2022 VENUE : Ramanujan Lecture Hall and online T
From playlist First-Passage Percolation and Related Models 2022 Edited
Stephen Wright: "Some Relevant Topics in Optimization, Pt. 2"
Graduate Summer School 2012: Deep Learning Feature Learning "Some Relevant Topics in Optimization, Pt. 2" Stephen Wright, University of Wisconsin-Madison Institute for Pure and Applied Mathematics, UCLA July 16, 2012 For more information: https://www.ipam.ucla.edu/programs/summer-school
From playlist GSS2012: Deep Learning, Feature Learning
What are Continuous Random Variables? (2 of 3: Why we need different tools)
More resources available at www.misterwootube.com
From playlist Random Variables
Continued fractions, the Chen-Stein method and extreme value theory by Parthanil Roy
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