Multidimensional signal processing | Theorems in Fourier analysis
In digital signal processing, multidimensional sampling is the process of converting a function of a into a discrete collection of values of the function measured on a discrete set of points. This article presents the basic result due to Petersen and Middleton on conditions for perfectly reconstructing a wavenumber-limited function from its measurements on a discrete lattice of points. This result, also known as the Petersen–Middleton theorem, is a generalization of the Nyquist–Shannon sampling theorem for sampling one-dimensional band-limited functions to higher-dimensional Euclidean spaces. In essence, the Petersen–Middleton theorem shows that a wavenumber-limited function can be perfectly reconstructed from its values on an infinite lattice of points, provided the lattice is fine enough. The theorem provides conditions on the lattice under which perfect reconstruction is possible. As with the Nyquist–Shannon sampling theorem, this theorem also assumes an idealization of any real-world situation, as it only applies to functions that are sampled over an infinitude of points. Perfect reconstruction is mathematically possible for the idealized model but only an approximation for real-world functions and sampling techniques, albeit in practice often a very good one. (Wikipedia).
What is multistage sampling? Examples, including real life examples. Advantages and disadvantages. Check out my e-book, Sampling in Statistics, which covers everything you need to know to find samples with more than 20 different techniques: https://prof-essa.creator-spring.com/listing/sam
From playlist Sampling
Multimeter Review / DMM Review / buyers guide / tutorial
A list of my multimeters can be purchased here: http://astore.amazon.com/m0711-20?_encoding=UTF8&node=5 In this video I do a review of several digital multimeters. I compare features and functionality. I explain safety features, number of digits, display count, accuracy and resolution. Th
From playlist Multimeter reviews, buyers guide and comparisons.
(PP 6.1) Multivariate Gaussian - definition
Introduction to the multivariate Gaussian (or multivariate Normal) distribution.
From playlist Probability Theory
08c Machine Learning: Multidimensional Scaling
Lecture on multidimensional scaling for feature projection. Reduce the dimensionality while preserving the dissimilarity between the training samples. Follow along with the demonstration workflow in Python's scikit-learn package: https://github.com/GeostatsGuy/PythonNumericalDemos/blob/ma
From playlist Machine Learning
Understanding Multidimensional Expressions (MDX) I MSBI | Edureka
"""Watch Sample Class Recording: http://www.edureka.co/microsoft-bi?utm_source=youtube&utm_medium=referral&utm_campaign=mdx-multi-dimensional-expressions The MultiDimensional eXpressions (MDX) language provides a specialized syntax for querying and manipulating the multidimensional data s
From playlist Microsoft BI Tutorial Videos
Naotoshi Nakamura - LAVENDER extracts individual variability...
Naotoshi Nakamura - LAVENDER extracts individual variability in the response to seasonal influenza vaccination The human immune system is known to be highly variable among individuals, but it is not well understood how the variability changes over time, especially when faced with externa
From playlist From Molecules and Cells to Human Health : Ideas and concepts
An overview of the most popular sampling methods used in statistics. Check out my e-book, Sampling in Statistics, which covers everything you need to know to find samples with more than 20 different techniques: https://prof-essa.creator-spring.com/listing/sampling-in-statistics
From playlist Sampling
Research Methods 1: Sampling Techniques
In this video, I discuss several types of sampling: random sampling, stratified random sampling, cluster sampling, systematic sampling, and convenience sampling. The figures presented are adopted/adapted from: https://www.pngkey.com/detail/u2y3q8q8e6o0u2t4_population-and-sample-graphic-de
From playlist Research Methods
Frequency Domain Interpretation of Sampling
http://AllSignalProcessing.com for more great signal-processing content: ad-free videos, concept/screenshot files, quizzes, MATLAB and data files. Analysis of the effect of sampling a continuous-time signal in the frequency domain through use of the Fourier transform.
From playlist Sampling and Reconstruction of Signals
[Research] Evaluation Examples Are Not Equally Informative: How Should That Change NLP Leaderboards?
Leaderboards are widely used in NLP and push the field forward. While leaderboards are a straightforward ranking of NLP models, this simplicity can mask nuances in evaluation items (examples) and subjects (NLP models). Rather than replace leaderboards, we advocate a re-imagining so that th
From playlist Research Talks
Gibbs Sampling : Data Science Concepts
Another MCMC Method. Gibbs sampling is great for multivariate distributions where conditional densities are *easy* to sample from. To emphasize a point in the video: - First sample is (x0,y0) - Next Sample is (x1,y1) - Next Sample is (x2,y2) ... That is, we update *all* variables once
From playlist Bayesian Statistics
Woojin Kim (6/15/20): Spatiotemporal persistent homology for dynamic metric spaces
Title: Spatiotemporal persistent homology for dynamic metric spaces Abstract: Characterizing the dynamics of time-evolving data within the framework of topological data analysis (TDA) has been attracting increasingly more attention. Popular instances of time-evolving data include flocking
From playlist ATMCS/AATRN 2020
Statistics Tutorial 4 Z Scores / Confidence Interval
New Video Everyday at 1 PM EST!!! In this tutorial I'll cover both Probability and Normal Distributions. I continue to cover Sample Error, the Central Limit Theorem, Z Scores, Confidence Intervals and more with code for everything. After my last tutorial on Statistics people were asking
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Caroline Chaux : L'échantillonnage
Recording during the thematic meeting : "Hommage à Claude Shannon" the November 2, 2016 at the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Guillaume Hennenfent Find this video and other talks given by worldwide mathematicians on CIRM's Audiovisual Math
From playlist Hommage/Tribute - Claude Shannon - Nov 2016
Nikos Sidiropoulos: "Supervised Learning and Canonical Decomposition of Multivariate Functions"
Tensor Methods and Emerging Applications to the Physical and Data Sciences 2021 Workshop III: Mathematical Foundations and Algorithms for Tensor Computations "Supervised Learning and Canonical Decomposition of Multivariate Functions (Joint work with Nikos Kargas)" Nikos Sidiropoulos - Uni
From playlist Tensor Methods and Emerging Applications to the Physical and Data Sciences 2021
What is Multivariate Testing? | Data Science in Minutes
In this tutorial, we will explain: how a multivariate test differs from an A/B Test, how to create and conduct a multivariate test, and what questions you should be asking of your test. Multivariate testing is a technique for testing a hypothesis in which multiple variables are modified.
From playlist Data Science in Minutes
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
What is "Probability sampling?" A brief overview. Four different types, their advantages and disadvantages: cluster, SRS (Simple Random Sampling), Systematic and Stratified sampling. Check out my e-book, Sampling in Statistics, which covers everything you need to know to find samples with
From playlist Sampling
Overview of non probability sampling; advantages and disadvantages, types. Check out my e-book, Sampling in Statistics, which covers everything you need to know to find samples with more than 20 different techniques: https://prof-essa.creator-spring.com/listing/sampling-in-statistics
From playlist Sampling
Deep Learning with Tensorflow - The MNIST Database
Enroll in the course for free at: https://bigdatauniversity.com/courses/deep-learning-tensorflow/ Deep Learning with TensorFlow Introduction The majority of data in the world is unlabeled and unstructured. Shallow neural networks cannot easily capture relevant structure in, for instance,
From playlist Deep Learning with Tensorflow