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
What is cluster sampling? Comparison to stratified sampling. 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/sampling-in
From playlist Sampling
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
Build a Heap - Intro to Algorithms
This video is part of an online course, Intro to Algorithms. Check out the course here: https://www.udacity.com/course/cs215.
From playlist Introduction to Algorithms
What is systematic sampling? Advantages and disadvantages. How to perform systematic sampling and repeated systematic sampling. 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.c
From playlist Sampling
STRATIFIED, SYSTEMATIC, and CLUSTER Random Sampling (12-4)
To create a Stratified Random Sample, divide the population into smaller subgroups called strata, then use random sampling within each stratum. Strata are formed based on members’ shared (qualitative) characteristics or attributes. Stratification can be proportionate to the population size
From playlist Sampling Distributions in Statistics (WK 12 - QBA 237)
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
Bayesian Inference in the Wolfram Language
To learn more about Wolfram Technology Conference, please visit: https://www.wolfram.com/events/technology-conference/ Speaker: Sjoerd Smit Wolfram developers and colleagues discussed the latest in innovative technologies for cloud computing, interactive deployment, mobile devices, and m
From playlist Wolfram Technology Conference 2017
Assumption-free prediction intervals for black-box regression algorithms - Aaditya Ramdas
Seminar on Theoretical Machine Learning Topic: Assumption-free prediction intervals for black-box regression algorithms Speaker: Aaditya Ramdas Affiliation: Carnegie Mellon University Date: April 21, 2020 For more video please visit http://video.ias.edu
From playlist Mathematics
Joshua Speagle - A Brief Introduction to Nested Sampling - IPAM at UCLA
Recorded 17 November 2021. Joshua Speagle of the University of Toronto presents "A Brief Introduction to Nested Sampling" at IPAM's Workshop III: Source inference and parameter estimation in Gravitational Wave Astronomy. Abstract: Quantifying model uncertainty and performing model selectio
From playlist Workshop: Source inference and parameter estimation in Gravitational Wave Astronomy
Anthony Nouy: Adaptive low-rank approximations for stochastic and parametric equations [...]
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 Numerical Analysis and Scientific Computing
Gravitational astronomy (Lecture 5) by B S Sathyaprakash
DATES Monday 25 Jul, 2016 - Friday 05 Aug, 2016 VENUE Madhava Lecture Hall, ICTS Bangalore APPLY Over the last three years ICTS has been organizing successful summer/winter schools on various topics of gravitational-wave (GW) physics and astronomy. Each school from this series aimed at foc
From playlist Summer School on Gravitational-Wave Astronomy
John Veitch - Computational Challenges in Gravitational Wave Parameter Estimation - IPAM at UCLA
Recorded 02 December 2021. John Veitch of the University of Glasgow, Physics and Astronomy, presents " Computational Challenges in Gravitational Wave Parameter Estimation" at IPAM's Workshop IV: Big Data in Multi-Messenger Astrophysics. Abstract: After detection, parameter estimation for c
From playlist Workshop: Big Data in Multi-Messenger Astrophysics
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
Monte Carlo Geometry Processing
Project Page: http://www.cs.cmu.edu/~kmcrane/Projects/MonteCarloGeometryProcessing/index.html
From playlist Research
Heap Sort - Intro to Algorithms
This video is part of an online course, Intro to Algorithms. Check out the course here: https://www.udacity.com/course/cs215.
From playlist Introduction to Algorithms
Deep Learning-accelerated cosmological inference from next-generation surveys-Alessio Spurio Mancini
Topic: COSMOPOWER: Deep Learning - accelerated cosmological inference from next-generation surveys. Speaker: Alessio Spurio Mancini Affiliation: University College London Date: May 9, 2022
From playlist IAS/PU Cosmology Discussion
Julien Tierny (2/3/22): Wasserstein Distances, Geodesics and Barycenters of Merge Trees
In this talk, I will present a unified computational framework for the estimation of distances, geodesics and barycenters of merge trees. We extend recent work on the edit distance and introduce a new metric, called the Wasserstein distance between merge trees, which is purposely designed
From playlist AATRN 2022
Statistics Lesson #1: Sampling
This video is for my College Algebra and Statistics students (and anyone else who may find it helpful). It includes defining and looking at examples of five sampling methods: simple random sampling, convenience sampling, systematic sampling, stratified sampling, cluster sampling. We also l
From playlist Statistics
GTAC 2015: Nest Automation Infrastructure
http://g.co/gtac Slides: https://docs.google.com/presentation/d/1x-l6xb9uUzFA9n0EFxcsvIEj6dQPQBcCu3JucAVe-8E/pub Usman Abdullah (Nest), Giulia Guidi (Nest) and Sam Gordon (Nest) Nest’s vision for the Thoughtful Home involves interconnected, intelligent devices working together to make yo
From playlist GTAC 2015