Inverse problems | Theory of probability distributions | Measures (measure theory)
In mathematics — specifically, in the fields of probability theory and inverse problems — Besov measures and associated Besov-distributed random variables are generalisations of the notions of Gaussian measures and random variables, Laplace distributions, and other classical distributions. They are particularly useful in the study of inverse problems on function spaces for which a Gaussian Bayesian prior is an inappropriate model. The construction of a Besov measure is similar to the construction of a Besov space, hence the nomenclature. (Wikipedia).
Measure Theory 2.1 : Lebesgue Outer Measure
In this video, I introduce the Lebesgue outer measure, and prove that it is, in fact, an outer measure. Email : fematikaqna@gmail.com Code : https://github.com/Fematika/Animations Notes : None yet
From playlist Measure Theory
Vernier caliper / diameter and length of daily used objects.
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From playlist Fine Measurements
Stéphane Seuret: Besov spaces in multifractal environment, and the Frisch-Parisi conjecture
Multifractal properties of data coming from many scientific fields (especially in turbulence) are now rigorously established. Unfortunately, the parameters measured on these data do not correspond to those mathematically obtained for the typical (or almost sure) functions in the standard f
From playlist Jean-Morlet Chair - Pollicott/Vaienti
Anthony Nouy: "Approximation and learning with tree tensor networks"
Tensor Methods and Emerging Applications to the Physical and Data Sciences 2021 Workshop I: Tensor Methods and their Applications in the Physical and Data Sciences "Approximation and learning with tree tensor networks" Anthony Nouy - Université de Nantes Abstract: Tree tensor networks (T
From playlist Tensor Methods and Emerging Applications to the Physical and Data Sciences 2021
Cornelia Schneider: Regularity in Besov spaces of parabolic PDEs
HYBRID EVENT This talk is concerned with the regularity of solutions to parabolic evolution equations. Special attention is paid to the smoothness in the specific scales $\ B^{r}_{\tau,\tau}, \ \frac{1}{\tau}=\frac{r}{d}+\frac{1}{p}\ $ of Besov spaces. The regularity in these spaces deter
From playlist Analysis and its Applications
Micrometer / diameter of daily used objects
What was the diameter? music: https://www.bensound.com/
From playlist Fine Measurements
Micrometer/diameter of daily used objects.
What was the diameter? music: https://www.bensound.com/
From playlist Fine Measurements
Francesco de Vecchi: Stochastic quantization of exponential quantum field theory
The lecture was held within the of the Hausdorff Junior Trimester Program: Randomness, PDEs and Nonlinear Fluctuations. Abstract: We give a review of the results on the elliptic and parabolic Euclidean stochastic quantization of the two-dimensional scalar field with exponential interactio
From playlist HIM Lectures: Junior Trimester Program "Randomness, PDEs and Nonlinear Fluctuations"
Gérard Kerkyacharian: Wavelet: from statistic to geometry
Find this video and other talks given by worldwide mathematicians on CIRM's Audiovisual Mathematics Library: http://library.cirm-math.fr. And discover all its functionalities: - Chapter markers and keywords to watch the parts of your choice in the video - Videos enriched with abstracts, b
From playlist 30 years of wavelets
Measure Theory 1.1 : Definition and Introduction
In this video, I discuss the intuition behind measures, and the definition of a general measure. I also introduce the Lebesgue Measure, without proving that it is indeed a measure. Email : fematikaqna@gmail.com Code : https://github.com/Fematika/Animations Notes : None yet
From playlist Measure Theory
Asymptotic efficiency in high-dimensional covariance estimation – V. Koltchinskii – ICM2018
Probability and Statistics Invited Lecture 12.18 Asymptotic efficiency in high-dimensional covariance estimation Vladimir Koltchinskii Abstract: We discuss recent results on asymptotically efficient estimation of smooth functionals of covariance operator Σ of a mean zero Gaussian random
From playlist Probability and Statistics
Luis VEGA - Remarks about the self - similar solutions of the Vortex Filament Equation
I will review some of the properties of the self-similar solutions of the Vortex Filament Equation. This equation is also known as either the Localized Induction Equation or the binormal flow and is related to the 1d Schrodinger map and the 1d cubic non-linear Sc
From playlist Trimestre "Ondes Non Linéaires" - May Conference
Kai Zeng - Schatten Properties of Commutators
Given a quantum tori $\mathbb{T}_{\theta}^d$, we can define the Riesz transforms $\mathfrak{R}_j$ on the quantum tori and the commutator $đx_i$ := [$\mathfrak{R}_i,M_x$], where $M_x$ is the operator on $L^2(\mathbb{T}_{\theta}^d)$ of pointwise multiplication by $x \in L^\infty (\mathbb{T}_
From playlist Annual meeting “Arbre de Noël du GDR Géométrie non-commutative”
Omega Squared with SPSS and Excel
Omega squared is a measure of effect size. How to calculate it using SPSS + Excel. Interpretation of omega squared, and when you shouldn't use it. SPSS doesn't calculate omega squared, but you can use the output from ANOVA to calculate the formula in Excel. 00:00 What is Omega Squared? 00:
From playlist SPSS
Measure Theory 2.2 : Lebesgue Measure of the Intervals
In this video, I prove that the Lebesgue measure of [a, b] is equal to the Lebesgue measure of (a, b) is equal to b - a. Email : fematikaqna@gmail.com Code : https://github.com/Fematika/Animations Notes : None yet
From playlist Measure Theory
What is a Bézier curve? Programmers use them everyday for graphic design, animation timing, SVG, and more. #shorts #animation #programming Animated Bézier https://www.jasondavies.com/animated-bezier/
From playlist CS101
Introduction to standard deviation, IQR [Inter-Quartile Range], and range
From playlist Unit 1: Descriptive Statistics
Lecture 8: Lebesgue Measurable Subsets and Measure
MIT 18.102 Introduction to Functional Analysis, Spring 2021 Instructor: Dr. Casey Rodriguez View the complete course: https://ocw.mit.edu/courses/18-102-introduction-to-functional-analysis-spring-2021/ YouTube Playlist: https://www.youtube.com/watch?v=cqdUuREzGuo&list=PLUl4u3cNGP63micsJp_
From playlist MIT 18.102 Introduction to Functional Analysis, Spring 2021
Lecture 9: Lebesgue Measurable Functions
MIT 18.102 Introduction to Functional Analysis, Spring 2021 Instructor: Dr. Casey Rodriguez View the complete course: https://ocw.mit.edu/courses/18-102-introduction-to-functional-analysis-spring-2021/ YouTube Playlist: https://www.youtube.com/watch?v=ETmIxkbTm3I&list=PLUl4u3cNGP63micsJp_
From playlist MIT 18.102 Introduction to Functional Analysis, Spring 2021
This video is about the measures of center, including the mean, median, and mode.
From playlist Statistical Measures