Gernot Akemann
Universität Bielefeld
March 19, 2014
For more videos, visit http://video.ias.edu

Views: 202
Institute for Advanced Study

Speaker: P. Vivo (King's College, London)
Spring College on the Physics of Complex Systems | (smr 3113)
2017_04_11-09_00-smr3113

Views: 3754
ICTP Condensed Matter and Statistical Physics

Leonid Pastur
B. Verkin Institute for Low Temperature Physics and Engineering of the National Academy of Sciences of Ukraine
April 16, 2014
We consider two classes of n×nn×n sample covariance matrices arising in quantum informatics. The first class consists of matrices whose data matrix has mm independent columns each of which is the tensor product of kk independent dd-dimensional vectors, thus n=dkn=dk. The matrices of the second class belong to n(ℂd1⊗ℂd2), n=d1d2Mn(Cd1⊗Cd2), n=d1d2 and are obtained from the standard sample covariance matrices by the partial transposition in ℂd2Cd2. We find that for the first class the limiting eigenvalue counting measure is the standard MP law despite the strong statistical dependence of the entries while for the second class the limiting eigenvalue counting measure is the shifted semicircle.
For more videos, visit http://video.ias.edu

Views: 141
Institute for Advanced Study

This video explains what is meant by the expectations and variance of a vector of random variables. Check out https://ben-lambert.com/econometrics-course-problem-sets-and-data/ for course materials, and information regarding updates on each of the courses. Quite excitingly (for me at least), I am about to publish a whole series of new videos on Bayesian statistics on youtube. See here for information: https://ben-lambert.com/bayesian/ Accompanying this series, there will be a book: https://www.amazon.co.uk/gp/product/1473916364/ref=pe_3140701_247401851_em_1p_0_ti

Views: 28179
Ben Lambert

The direct product is a way to combine two groups into a new, larger group. Just as you can factor integers into prime numbers, you can break apart some groups into a direct product of simpler groups.
If you’d like to help us make videos more quickly, you can support us on Patreon at
https://www.patreon.com/socratica
We also welcome Bitcoin donations! Our Bitcoin address is:
1EttYyGwJmpy9bLY2UcmEqMJuBfaZ1HdG9
Thank you!!
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We recommend the following textbooks:
Dummit & Foote, Abstract Algebra 3rd Edition
http://amzn.to/2oOBd5S
Milne, Algebra Course Notes (available free online)
http://www.jmilne.org/math/CourseNotes/index.html
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Be sure to subscribe so you don't miss new lessons from Socratica:
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Teaching Assistant: Liliana de Castro
Written & Directed by Michael Harrison
Produced by Kimberly Hatch Harrison

Views: 61185
Socratica

Yan Fyodorov
Queen Mary University of London
October 3, 2013
I will start with discussing the relation between a class of disorder-generated multifractals and logarithmically-correlated random fields and processes. An important example of the latter is provided by the so-called "1/f noise" which, in particular, emerges naturally in studies of characteristic polynomials of CUE matrices. Extending the consideration to GUE setting reveals more processes of that type, in particular a special singular limit of the Fractional Brownian Motion. In the rest of the talk I will attempt to show how to use heuristic insights from Statistical Mechanics of disordered systems to arrive to detailed conjectures about distributions of high and extreme values of logarithmically correlated processes and multifractals, including the absolute maximum of the Riemann zeta-function in intervals of the critical line.
For more videos, visit http://video.ias.edu

Views: 229
Institute for Advanced Study

Thanks to all of you who support me on Patreon. You da real mvps! $1 per month helps!! :) https://www.patreon.com/patrickjmt !! Graph Theory - An Introduction! In this video, I discuss some basic terminology and ideas for a graph: vertex set, edge set, cardinality, degree of a vertex, isomorphic graphs, adjacency lists, adjacency matrix, trees and circuits.
There is a MISTAKE on the adjacency matrix; I put a 1 in the v5 row and v5 column, but it should be placed in the v5 row and the v6 column. There are annotations pointing this out along with the corrected matrix!

Views: 442453
patrickJMT

This course is on Lemma: http://lem.ma Lemma looking for developers: http://lem.ma/jobs
Other than http://lem.ma, I recommend Strang http://bit.ly/StrangYT, Gelfand http://bit.ly/GelfandYT, and my short book of essays http://bit.ly/HALAYT
Questions and comments below will be promptly addressed.
Linear Algebra is one of the most important subjects in mathematics. It is a subject with boundless practical and conceptual applications.
Linear Algebra is the fabric by which the worlds of geometry and algebra are united at the most profound level and through which these two mathematical worlds make each other far more powerful than they ever were individually.
Virtually all subsequent subjects, including applied mathematics, physics, and all forms of engineering, are deeply rooted in Linear Algebra and cannot be understood without a thorough understanding of Linear Algebra. Linear Algebra provides the framework and the language for expressing the most fundamental relationships in virtually all subjects.
This collection of videos is meant as a stand along self-contained course. There are no prerequisites. Our focus is on depth, understanding and applications. Our innovative approach emphasizes the geometric and algorithmic perspective and was designed to be fun and accessible for learners of all levels.
Numerous exercises will be provided via the Lemma system (under development)
We will cover the following topics:
Vectors
Linear combinations
Decomposition
Linear independence
Null space
Span
Linear systems
Gaussian elimination
Matrix multiplication and matrix algebra
The inverse of a matrix
Elementary matrices
LU decomposition
LDU decomposition
Linear transformations
Determinants
Cofactors
Eigenvalues
Eigenvectors
Eigenvalue decomposition (also known as the spectral decomposition)
Inner product (also known as the scalar product and dot product)
Self-adjoint matrices
Symmetric matrices
Positive definite matrices
Cholesky decomposition
Gram-Schmidt orthogonalization
QR decomposition
Elements of numerical linear algebra
I’m Pavel Grinfeld. I’m an applied mathematician. I study problems in differential geometry, particularly with moving surfaces.

Views: 1641
MathTheBeautiful

This video provides an introduction as to how we can derive the variance-covariance matrix for a set of indicator variables, when we use the matrix notation form of factor analysis models. Check out https://ben-lambert.com/econometrics-course-problem-sets-and-data/ for course materials, and information regarding updates on each of the courses. Quite excitingly (for me at least), I am about to publish a whole series of new videos on Bayesian statistics on youtube. See here for information: https://ben-lambert.com/bayesian/ Accompanying this series, there will be a book: https://www.amazon.co.uk/gp/product/1473916364/ref=pe_3140701_247401851_em_1p_0_ti

Views: 78151
Ben Lambert

This video explains what is meant by the covariance and correlation between two random variables, providing some intuition for their respective mathematical formulations. Check out https://ben-lambert.com/econometrics-course-problem-sets-and-data/ for course materials, and information regarding updates on each of the courses. Quite excitingly (for me at least), I am about to publish a whole series of new videos on Bayesian statistics on youtube. See here for information: https://ben-lambert.com/bayesian/ Accompanying this series, there will be a book: https://www.amazon.co.uk/gp/product/1473916364/ref=pe_3140701_247401851_em_1p_0_ti

Views: 285494
Ben Lambert

MIT 6.046J Design and Analysis of Algorithms, Spring 2015
View the complete course: http://ocw.mit.edu/6-046JS15
Instructor: Srinivas Devadas
In this lecture, Professor Devadas introduces randomized algorithms, looking at solving sorting problems with this new tool.
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu

Views: 21380
MIT OpenCourseWare

This video shows you how to do matrix calculation such as Matrix Determinant, matrix inverse, matrix addition, matrix multiplication, transposition and more.
Quick starter guide: http://bit.ly/2WDxx5z
Tutorial by Equaser
I need your help. I appreciate it.
Please visit my other channel and subscribe.
http://bit.ly/2tGgYc4
20180325-AD

Views: 65048
Equaser.com

Jelani Nelson
Member, School of Mathematics, Institute for Advanced Study
March 11, 2013
fundamental theorem in linear algebra is that any real n x d matrix has a singular value decomposition (SVD). Several important numerical linear algebra problems can be solved efficiently once the SVD of an input matrix is computed: e.g. least squares regression, low rank approximation, and computing preconditioners, just to name a few.
Unfortunately in many modern big data applications the input matrix is very large, so that computing the SVD is computationally expensive.
Following a line of work of [Sarlós, 2006] and [Clarkson and Woodruff, 2013] on using dimensionality reduction techniques for solving numerical linear algebra problems, we show that several such problems with input matrices of size n x d can be automatically transformed into a new instance of the problem that has size very close to d x d, and thus can be solved much more quickly. Furthermore, executing these transformations require time only linear, or nearly linear, in the number of non-zero entries in the input matrix. Our techniques are of independent interest in random matrix theory, and the main technical contribution of our work turns out to be an analysis of the smallest and largest eigenvalues of certain random matrices.
This talk is based on joint work with Huy Lê Nguyen (Princeton).
For more videos, visit http://video.ias.edu

Views: 427
Institute for Advanced Study

Benoît Collins's talk from the "Noncommutative L^p Spaces, Operator Spaces, and Applications" workshop held at Banff in June of 2010.
The conference website, where the videos can be downloaded, can be found here:
http://www.birs.ca/events/2010/5-day-workshops/10w5005/videos

Views: 329
LeonhardEuler1

This video shows you how to use np.random.random(()) to create random arrays of size n with entries between any numbers you desire.
This is a Python anaconda tutorial for help with coding, programming, or computer science. These are short python videos dedicated to troubleshooting python problems and learning Python syntax. For more videos see Python Help playlist by Rylan Fowers.
✅Subscribe: https://www.youtube.com/channel/UCub4qT8Sgm7ytZsO-jLL4Ow?sub_confirmation=1
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▶️Watch Latest Python Content: https://www.youtube.com/watch?v=myCPgAO9BgQ&list=PLL3Qv26_SCsGWTF5PRaWUY0yhURFvco7L
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🐦Follow Rylan on Twitter: https://twitter.com/rylanpfowers
The creator studies Applied and Computational Mathematics at BYU (BYU ACME or BYU Applied Math) and does work for the BYU Chemical Engineering Department
RANDOM ARRAY
In this video let me show you some examples of how to create random matrices in python.
We import numpy as np and random
First just type np.random.ranom and then enter a tuple with the size of the disred random matrix. It will automatically create a matrix with entries between 1 and 0
If we want to change the value range, we can multiply by twice the value desired and then subtract the value desired from the end
So if we want entries between -5 and 5 we multiply by 10 and minus 5.
So if you think about it this way, the max number it could be is 10 - 5 = 5 and the min number it could be is 0 - 5 = -5
So if we want entries between -100 and 100 we multiply by 200 and minus 100.
So if you think about it this way, the max number it could be is 200 - 100 = 100 and the min number it could be is 0 - 100 = -100
There you have it, that is how you create random arrays in python

Views: 666
Rylan Fowers

Multiplying two 2x2 matrices.
Practice this yourself on Khan Academy right now: https://www.khanacademy.org/e/multiplying_a_matrix_by_a_matrix?utm_source=YTdescription&utm_medium=YTdescription&utm_campaign=YTdescription

Views: 1108472
Khan Academy

Balint Virag
Univ Toronto
April 1, 2014
For more videos, visit http://video.ias.edu

Views: 268
Institute for Advanced Study

Theoretical analysis of Spectral Clustering is often based on the theory of random matrices which assumes that all entries of the data matrix (with each row representing a data object) are independent random variables. In practice, however, while the objects may be independent of each other, features of an object are not independent. To address this, we will prove bounds on the singular values of a matrix assuming only that its rows are independent vector-valued random variables (the columns may not be independent) and describe a new clustering algorithm with this limited independence. In the second part, we address a more theoretical question. We will show a generalization of Azuma's (martingale) inequality to the case when the random variables are matrices and more generally infinite-dimensional operators. The first part is joint work with A. Dasgupta, J. Hopcroft and P. Mitra.

Views: 202
Microsoft Research

This instructional video demonstrates the uses of MINVERSE and MMULT functions of Microsoft Excel to find the product and inverse of matrices. It is developed for my operations research classes.
Note: The videos on this channel are instructional videos developed for the classes that I teach at the department of Industrial Engineering, Morgan State University in Baltimore Maryland.

Views: 2368
DrSalimian

This talk was given on Friday, November 17, at the CDM conference at Harvard University

Views: 1232
Harvard Math

What the inverse of a matrix is. Examples of inverting a 2x2 matrix.
Practice this lesson yourself on KhanAcademy.org right now:
https://www.khanacademy.org/math/precalculus/precalc-matrices/inverting_matrices/e/matrix_inverse_2x2?utm_source=YT&utm_medium=Desc&utm_campaign=Precalculus
Watch the next lesson: https://www.khanacademy.org/math/precalculus/precalc-matrices/inverting_matrices/v/matrices-to-solve-a-system-of-equations?utm_source=YT&utm_medium=Desc&utm_campaign=Precalculus
Missed the previous lesson?
https://www.khanacademy.org/math/precalculus/precalc-matrices/inverting_matrices/v/inverse-of-a-2x2-matrix?utm_source=YT&utm_medium=Desc&utm_campaign=Precalculus
Precalculus on Khan Academy: You may think that precalculus is simply the course you take before calculus. You would be right, of course, but that definition doesn't mean anything unless you have some knowledge of what calculus is. Let's keep it simple, shall we? Calculus is a conceptual framework which provides systematic techniques for solving problems. These problems are appropriately applicable to analytic geometry and algebra. Therefore....precalculus gives you the background for the mathematical concepts, problems, issues and techniques that appear in calculus, including trigonometry, functions, complex numbers, vectors, matrices, and others. There you have it ladies and gentlemen....an introduction to precalculus!
About Khan Academy: Khan Academy offers practice exercises, instructional videos, and a personalized learning dashboard that empower learners to study at their own pace in and outside of the classroom. We tackle math, science, computer programming, history, art history, economics, and more. Our math missions guide learners from kindergarten to calculus using state-of-the-art, adaptive technology that identifies strengths and learning gaps. We've also partnered with institutions like NASA, The Museum of Modern Art, The California Academy of Sciences, and MIT to offer specialized content.
For free. For everyone. Forever. #YouCanLearnAnything
Subscribe to Khan Academy’s Precalculus channel:
https://www.youtube.com/channel/UCBeHztHRWuVvnlwm20u2hNA?sub_confirmation=1
Subscribe to Khan Academy: https://www.youtube.com/subscription_center?add_user=khanacademy

Views: 1133126
Khan Academy

MATRICES & DETERMINANTS BIGGEST MISTAKES/ ERROR Class 12th CBSE 2019. Most Important and Previous year questions solved.
#CBSE2019 #CBSEMath2019 #MatricesAnd DeterminantsCBSE #BiggesterroesClass12 #CBSEMath12
PLAYLIST FOR VECTORS AND THREE DIMENSIONAL GEOMETRY
https://www.youtube.com/playlist?list=PLTa2m6WeLW_-mhXBuPcd93jPGLDSjxSks
PLAYLIST FOR MATRICES AND DETERMINANTS
https://www.youtube.com/playlist?list=PLTa2m6WeLW_9J9Jif5iQMFZ5aaGymwk-M
PLAYLIST FOR LINEAR PROGRAMMING PROBLEM (LPP)
https://www.youtube.com/playlist?list=PLTa2m6WeLW_-Ph8k4lIl3kKpTFWO4TpSt
PLAYLIST FOR RELATIONS AND FUNCTIONS
https://www.youtube.com/playlist?list=PLTa2m6WeLW_9i-1xiAwZ23U57vRobeJ0J
PLAYLIST FOR PROBABILITY
https://www.youtube.com/playlist?list=PLTa2m6WeLW_8FXXvlcbS5cbx9ZLjDJMGi
PLAYLIST FOR INTEGRATION
https://www.youtube.com/playlist?list=PLTa2m6WeLW_8ZHPXS1YIbweyg35ytmByS
PLAYLIST FOR APPLICATION OF DERIVATIVES
https://www.youtube.com/playlist?list=PLTa2m6WeLW__9QqBNd9RT9FDHs9x6z3p6
PLAYLIST FOR DIFFERENTIATION/DERIVATIVES
https://www.youtube.com/playlist?list=PLTa2m6WeLW_8TLgSrV-g1NOzViEfZGIdY
PLAYLIST FOR CONTINUITY
https://www.youtube.com/playlist?list=PLTa2m6WeLW_8KsyD5M3LoY9YxMMcRok75
PLAYLIST FOR INVERSE TRIGONOMETRIC FUNCTIONS
https://www.youtube.com/playlist?list=PLTa2m6WeLW_-esFJBOyg225Dh-9wQi_D6
#CBSE2019Math #BinaryOperations12th #RelationsAndFunctions
© Copyright 2019, Neha Agrawal. All rights reserved.
You can now follow me on FACEBOOK and TWITTER as well. Click on the link-
https://www.facebook.com/NehaAgrawalMath/
Twitter: https://twitter.com/NehaAgrawalMath
WHATSAPP: +91 9870380964
E-MAIL: [email protected]

Views: 38944
Neha Agrawal Mathematically Inclined

Interacting systems of many quantum particles exhibit rich physics due to their underlying entanglement, and are a topic of major interest in several areas of physics. In recent years, quantum information ideas have allowed us to understand the entanglement structure of such systems, and to come up with novel ways to describe and study them. In my lecture, I will first explain how we can describe such systems based on their entanglement structure, giving rise to so-called Tensor Network States. I will then discuss how these concepts can be used to model strongly interacting many-body systems and to study the different exotic topological states of matter based on their entanglement, and I will briefly highlight their suitability for numerical simulations. Finally, I will discuss open mathematical and physical challenges in the field.

Views: 1104
Microsoft Research

Razvan Gurau / 23.10.17
Invitation to Random Tensors
Random matrices are ubiquitous in modern theoretical physics and provide insights on a wealth of phenomena, from the spectra of heavy nuclei to the theory of strong interactions or random two dimensional surfaces. The backbone of all the analytical results in matrix models is their 1/N expansion (where N is the size of the matrix). Despite early attempts in the '90, the generalization of this 1/N expansion to higher dimensional random tensor models has proven very challenging. This changed with the discovery of the 1/N expansion (originally for colored and subsequently for arbitrary invariant) tensor models in 2010. I this talk I will present a short introduction to the modern theory of random tensors and its connections to conformal field theory and random higher dimensional geometry.
----------------------------------
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Facebook : https://www.facebook.com/InstitutHenriPoincare/
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Views: 174
Institut Henri Poincaré

Speaker: Horng-Tzer Yau (Harvard University, USA)

Views: 629
NCTS Math Division

Markov Matrices
Instructor: David Shirokoff
View the complete course: http://ocw.mit.edu/18-06SCF11
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu

Views: 97892
MIT OpenCourseWare

Title: Limit theorems for eigenvectors of the normalized Laplacian for random graphs
by Minh Hai Tang from Johns Hopkins University
Abstract: "We prove a central limit theorem for the components of the eigenvectors corresponding to the d largest eigenvalues of the normalized Laplacian matrix of a finite dimensional random dot product graph. As a corollary, we show that for stochastic blockmodel graphs, the rows of the spectral embedding of the normalized Laplacian converge to multivariate normals and furthermore the mean and the covariance matrix of each row are functions of the associated vertex's block membership. Together with prior results for the eigenvectors of the adjacency matrix, we then compare, via the Chernoff information between multivariate normal distributions, how the choice of embedding method impacts subsequent inference. We demonstrate that neither embedding method dominates with respect to the inference task of recovering the latent block assignments.”

Views: 90
Experimental mathematics

In this video tutorial we will understand the concept of multidimensional Arrays in C++ and perform some basic addition and subtraction operations on 2D arrays in c++.
2D Arrays Theory Article - https://simplesnippets.tech/cpp-multidimensional-arrays-2d-arrays/
Download Dev C++ IDE : https://sourceforge.net/projects/orwe...
Download C++ Android App : https://play.google.com/store/apps/details?id=learn.cplusplus.programminglanguage&hl=en
Simple Snippets Official Website -
https://simplesnippets.tech/
Simple Snippets on Facebook-
https://www.facebook.com/simplesnippets/
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For Classroom Coaching in Mumbai for Programming & other IT/CS Subjects Checkout UpSkill Infotech - https://upskill.tech/
UpSkill is an Ed-Tech Company / Coaching Centre for Information Technology / Computer Science oriented courses and offer coacing for various Degree courses like BSc.IT, BSc.CS, BCA, MSc.IT, MSc.CS, MCA etc.
Contact via email /call / FB /Whatsapp for more info
email - [email protected]
We also Provide Certification courses like -
Android Development
Web Development
Java Developer Course
.NET Developer Course

Views: 15780
Simple Snippets

In this video, you will learn the fundamental concept of matrix multiplication from scratch.
You can find the code in the Github link below:
https://github.com/mohendra/My_Projects/tree/master/python

Views: 4910
AI Medicines

Van Vu
Rutgers University
June 17, 2010
For more videos, visit http://video.ias.edu

Views: 121
Institute for Advanced Study

Properties of the multivariate Gaussian probability distribution

Views: 98318
Alexander Ihler

This video explains what is meant by the expectations and variance of a vector of random variables. Check out https://ben-lambert.com/econometrics-course-problem-sets-and-data/ for course materials, and information regarding updates on each of the courses. Quite excitingly (for me at least), I am about to publish a whole series of new videos on Bayesian statistics on youtube. See here for information: https://ben-lambert.com/bayesian/ Accompanying this series, there will be a book: https://www.amazon.co.uk/gp/product/1473916364/ref=pe_3140701_247401851_em_1p_0_ti

Views: 19137
Ben Lambert

Project Name: Mathematical sciences without walls
Project Investigator: Dr. R. Ramanujam
Module Name: Limited theorems for Spectral Statistics of Large Random Matrices by Leonid Pastur

Views: 28
Vidya-mitra

'''
Matrices and Vector with Python
Session # 1
Topic to be covered -
1. How to create Matrices
2. How to create random matrices of different orders
3. How to access the matrices elements
4. How to delete rows and column of a matrix.
'''
'''
Q) What is Matrix?
Matrix is a rectangular array of numbers, symbols or expression arranged in rows and columsn.
Q) Where do we use matrix in Machine Learning?
Matrix are used to read the input data which is in the form of .csv, .txt, .xml and other formats.
It is especially used to processed as the input data varible (X) when training the algorithm.
'''
import numpy as np
#1. How to create Matrices
matrix = np.array([[3,4],
[5,8]])
# How to create using random
#2. How to create random matrices of different orders
#import random
print(np.random.random((2,2)))
print(np.random.random((3,3)))
print(np.random.random_integers(0,9,(2,2)))
print(np.random.randint(0,100,(5,5)))
# 3. How to access the matrices elements
x = np.random.randint(0,100,(5,5))
# Extract the first column
x[:,0]
# Extract the Second Column
x[:,1]
# Extract the first row
x[0]
x[2]
# How to extract the 2nd and 4th row
x[[2,4]]
# How to extract the 1st and 4th Column
y = x[:,[1,4]]
##############################################################################
# 4. How to delete rows and column of a matrix.
# How to delete the second row
np.delete(x,[1],0)
# How to delete the second column
np.delete(x,[1],1)
# Delete second and third row
np.delete(x,[[2,3]],0)
# Delete second and third column
np.delete(x,[[2,3]],1)

Views: 358
MachineLearning with Python

Wiki: http://wiki.planetchili.net/index.php?title=Advanced_C%2B%2B_Programming_Tutorial_5
Patreon: https://www.patreon.com/planetchili

Views: 1841
ChiliTomatoNoodle

Check out my Blog:
http://exceltraining101.blogspot.com
If you've taken business class or familiar with management consulting strategies, you've probably come across this tool called a BCG Matrix. Also known as a growth-share matrix, the BCG matrix was created by Bruce Hendersen in the 70s (founder of Boston Consulting Group). It's a tool that helps you analyze companies or products based on a quadrant that shows growth rate and relative market share. You can actually create this fairly easily on Excel, so check out the video to learn how.
#exceltips
#exceltipsandtricks
#exceltutorial
#doughexcel

Views: 210173
Doug H

This video explains how to derive GLS estimators in matrix form.
Check out http://oxbridge-tutor.co.uk/graduate-econometrics-course/ for course materials, and information regarding updates on each of the courses. Check out https://ben-lambert.com/econometrics-course-problem-sets-and-data/ for course materials, and information regarding updates on each of the courses. Quite excitingly (for me at least), I am about to publish a whole series of new videos on Bayesian statistics on youtube. See here for information: https://ben-lambert.com/bayesian/ Accompanying this series, there will be a book: https://www.amazon.co.uk/gp/product/1473916364/ref=pe_3140701_247401851_em_1p_0_ti

Views: 17067
Ben Lambert

"What Does It Mean To Permute A Matrix?
Watch more videos for more knowledge
3.1.17-Linear Algebra: Permutation Matrix - YouTube https://www.youtube.com/watch/lOjawd_NzMA
GT17.1. Permutation Matrices - YouTube https://www.youtube.com/watch/skkiguCn8XA
Which Permutation makes Matrix Product P*A ... https://www.youtube.com/watch/X_mUzsRUDS0
What Does It Mean To Permute A Matrix? - YouTube https://www.youtube.com/watch/Nt6ml-lK254
Explain Permutation Matrix for row swap - YouTube https://www.youtube.com/watch/WiPMuIf3Qeg
Permutation matrix - YouTube https://www.youtube.com/watch/URd2cfJL61w
2.6 Linear Alzebra : Matrix Permutation - YouTube https://www.youtube.com/watch/mOc5vAe_TyE
Example of finding matrix inverse | Matrix ... https://www.youtube.com/watch/r9aTLTN16V4
Permutation matrix for row/column swap - YouTube https://www.youtube.com/watch/pCBRUW8kA9s
Inversions and Even Odd Permutations - YouTube https://www.youtube.com/watch/CjY1wIWL5kE
7.2.3 Permutation Matrices Part 1 - YouTube https://www.youtube.com/watch/GXbUXzc98L0
7.2.3 Permutation Matrices Part 2 - YouTube https://www.youtube.com/watch/aY-MBucLmh8
Random Vectors, Random Matrices, Permuted ... https://www.youtube.com/watch/OZC9a84XX9U
Groups of Permutations - YouTube https://www.youtube.com/watch/XTHcTT8YFiw
Graph Theory FAQs: 03. Isomorphism Using ... https://www.youtube.com/watch/UCle3Smvh1s
permutation and identity matrices - YouTube https://www.youtube.com/watch/Blq9g0I0rXg
LU Factorization with permutation matrix - YouTube https://www.youtube.com/watch/zporebWG4VQ
Product of two Permutation In Hindi - YouTube https://www.youtube.com/watch/f6qKLkGEwUQ
Linear Algebra - Matrix Transpose - YouTube https://www.youtube.com/watch/1Ux6GlQt-jw
"

Views: 2
Tip Tip 3

cross product of two matrices in matlab

Views: 15
Asap Games

In this video I show you how to calculate the inverse of a matrix on a Casio ClassWiz fx-991ex calculator when doing matrix algebra.
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