Multidimensional signal processing
Multidimension spectral estimation is a generalization of spectral estimation, normally formulated for one-dimensional signals, to multidimensional signals or multivariate data, such as wave vectors. (Wikipedia).
Pavle Blagojević (6/29/17) Bedlewo: Shadows of Cohen's Vanishing theorem
The overwhelming material of the seminal Springer Lecture Notes 533 is signed by Cohen, Lada and May. Page 268 hides the Vanishing theorem of Frederick Cohen. Both the result and the proof spreading over seven pages look technical. The Vanishing theorem states that the Serre spectral seque
From playlist Applied Topology in Będlewo 2017
Multidimensional spectroscopy with quantum light and in optical cavities by Shaul Mukamel
Open Quantum Systems DATE: 17 July 2017 to 04 August 2017 VENUE: Ramanujan Lecture Hall, ICTS Bangalore There have been major recent breakthroughs, both experimental and theoretical, in the field of Open Quantum Systems. The aim of this program is to bring together leaders in the Open Q
From playlist Open Quantum Systems
How to Evaluate a Multivariable Function Defined by an Integral
How to Evaluate a Multivariable Function Defined by an Integral 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 Thank you:)
From playlist Calculus 3
Evaluating the composition of Functions
👉 Learn how to evaluate an expression with the composition of a function and a function inverse. Just like every other mathematical operation, when given a composition of a trigonometric function and an inverse trigonometric function, you first evaluate the one inside the parenthesis. We
From playlist Evaluate a Composition of Inverse Trigonometric Functions
Evaluating the composition of Functions
👉 Learn how to evaluate an expression with the composition of a function and a function inverse. Just like every other mathematical operation, when given a composition of a trigonometric function and an inverse trigonometric function, you first evaluate the one inside the parenthesis. We
From playlist Evaluate a Composition of Inverse Trigonometric Functions
Evaluating the composition of Functions
👉 Learn how to evaluate an expression with the composition of a function and a function inverse. Just like every other mathematical operation, when given a composition of a trigonometric function and an inverse trigonometric function, you first evaluate the one inside the parenthesis. We
From playlist Evaluate a Composition of Inverse Trigonometric Functions
Evaluating the composition of Functions
👉 Learn how to evaluate an expression with the composition of a function and a function inverse. Just like every other mathematical operation, when given a composition of a trigonometric function and an inverse trigonometric function, you first evaluate the one inside the parenthesis. We
From playlist Evaluate a Composition of Inverse Trigonometric Functions
Evaluating the composition of Functions
👉 Learn how to evaluate an expression with the composition of a function and a function inverse. Just like every other mathematical operation, when given a composition of a trigonometric function and an inverse trigonometric function, you first evaluate the one inside the parenthesis. We
From playlist Evaluate a Composition of Inverse Trigonometric Functions
Evaluating the composition of Functions
👉 Learn how to evaluate an expression with the composition of a function and a function inverse. Just like every other mathematical operation, when given a composition of a trigonometric function and an inverse trigonometric function, you first evaluate the one inside the parenthesis. We
From playlist Evaluate a Composition of Inverse Trigonometric Functions
Ming Yuan: "Low Rank Tensor Methods in High Dimensional Data Analysis (Part 1/2)"
Watch part 2/2 here: https://youtu.be/5IA4z9On3Mg Tensor Methods and Emerging Applications to the Physical and Data Sciences Tutorials 2021 "Low Rank Tensor Methods in High Dimensional Data Analysis (Part 1/2)" Ming Yuan - Columbia University, Statistics Abstract: Large amount of multid
From playlist Tensor Methods and Emerging Applications to the Physical and Data Sciences 2021
Regression Analysis by Dr. Soumen Maity,Department of Mathematics,IIT Kharagpur.For more details on NPTEL visit http://nptel.ac.in
From playlist IIT Kharagpur: Regression Analysis | CosmoLearning.org Mathematics
Deep Inversion, Autoencoders for Learned Regularization (...) - Brune - Workshop 3 - CEB T1 2019
Christoph Brune (University of Twente) / 03.04.2019 Deep Inversion, Autoencoders for Learned Regularization of Inverse Problems. This talk will highlight how deep learning, inverse problems theory and the calculus of variations can profit from each other. Data-driven deep learning metho
From playlist 2019 - T1 - The Mathematics of Imaging
Ming Yuan: "Low Rank Tensor Methods in High Dimensional Data Analysis (Part 2/2)"
Watch part 1/2 here: https://youtu.be/K8t24xm7tn8 Tensor Methods and Emerging Applications to the Physical and Data Sciences Tutorials 2021 "Low Rank Tensor Methods in High Dimensional Data Analysis (Part 2/2)" Ming Yuan - Columbia University, Statistics Abstract: Large amount of multid
From playlist Tensor Methods and Emerging Applications to the Physical and Data Sciences 2021
Reliability 3: Cronbach's alpha and internal reliability
In this video, I discuss Cronbach's alpha and internal reliability. I also demonstrate how to compute Cronbach's alpha in SPSS and make sense of SPSS output such as corrected-item-total correlation, squared multiple correlations, and Cronbach's alpha if item deleted.
From playlist Reliability analysis
Composition of inverses using a triangle with variables
👉 Learn how to evaluate an expression with the composition of a function and a function inverse. Just like every other mathematical operation, when given a composition of a trigonometric function and an inverse trigonometric function, you first evaluate the one inside the parenthesis. We
From playlist Evaluate a Composition of Inverse Trigonometric Functions
Giovanni Peccati: Some applications of variational techniques in stochastic geometry III
Second-order Poincaré inequalities and related convergence results I will describe a new collection of probabilistic bounds on the Poisson space, allowing one to mea- sure the distance to Gaussianity for (possibly multidimensional) random elements displaying a form of ’two-scale stabiliza
From playlist Winter School on the Interplay between High-Dimensional Geometry and Probability
R - Item Response Theory Analysis Lecture
Lecturer: Dr. Erin M. Buchanan Missouri State University Summer 2016 This lecture covers Item Factor Analysis and Item Response Theory from the Beaujean SEM in R book. IRT information also pulled from StatsCamp materials taught by William Skorupski (highly recommend his class!). Both dic
From playlist Structural Equation Modeling
Sparse matrices in sparse analysis - Anna Gilbert
Members' Seminar Topic: Sparse matrices in sparse analysis Speaker: Anna Gilbert Affiliation: University of Michigan; Member, School of Mathematics Date: October 28, 2019 For more video please visit http://video.ias.edu
From playlist Mathematics
How to evaluate for the composition of two trigonometric functions
👉 Learn how to evaluate an expression with the composition of a function and a function inverse. Just like every other mathematical operation, when given a composition of a trigonometric function and an inverse trigonometric function, you first evaluate the one inside the parenthesis. We
From playlist Evaluate a Composition of Inverse Trigonometric Functions
Multidimensional Rasch measurement with ConQuest Software | A quick and effective guide
In this video, I demonstrate how to conduct a multidimensional Rasch measurement using the ConQuest software. For extensive reviews of Rasch measurement and item response theory (IRT) analysis, please read: Rasch: https://journals.sagepub.com/doi/full/10.1177/0265532220927487 IRT: https:
From playlist Rasch Measurement