This lesson introduces the different sample methods when conducting a poll or survey. Site: http://mathispower4u.com
From playlist Introduction to Statistics
Random Processes and Stationarity
http://AllSignalProcessing.com for more great signal-processing content: ad-free videos, concept/screenshot files, quizzes, MATLAB and data files. Introduction to describing random processes using first and second moments (mean and autocorrelation/autocovariance). Definition of a stationa
From playlist Random Signal Characterization
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
Random Sampling - Statistical Inference
In this video I talk about Random Sampling - I give you a full, in-depth primer about random sampling and what sampling is in general. I then discuss the two ways of taking a random sample from a population (1st way: No replacement; 2nd way: With replacement) and point out the difference b
From playlist Statistical Inference
SIMPLE Random Sampling Methods (12-3)
We want a representative sample. The best way to get a representative sample is to use a random sample. The best way to get a random sample is to use random sampling techniques. We can also use non-random sampling techniques. But…selecting a random sample does not guarantee it will be a re
From playlist Sampling Distributions in Statistics (WK 12 - QBA 237)
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)
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
Sampling (4 of 5: Introductory Examples of Stratified Random Sampling)
More resources available at www.misterwootube.com
From playlist Data Analysis
Analog vs. Digital Epsilons: Implementation Considerations Considerations for Differential Privacy
A Google TechTalk, presented by Olya Ohrimenko, 2021/11/17 Differential Privacy for ML series.
From playlist Differential Privacy for ML
“Data-Driven Pricing” – Prof. Omar Besbes
Pricing is central to many industries and academic disciplines ranging from Operations Research to Economics and Computer Science. At the heart of pricing lies a fundamental informational dimension regarding the level of knowledge about customers' values. In practice, the latter comes from
From playlist Thematic Program on Stochastic Modeling: A Focus on Pricing & Revenue Management
Randomness - Applied Cryptography
This video is part of an online course, Applied Cryptography. Check out the course here: https://www.udacity.com/course/cs387.
From playlist Applied Cryptography
Martin Wainwright: Privacy and statistical minimax: quantitative tradeoffs
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 Probability and Statistics
Lec 17 | MIT 3.320 Atomistic Computer Modeling of Materials
Monte Carlo Simulations: Application to Lattice Models, Sampling Errors, Metastability View the complete course at: http://ocw.mit.edu/3-320S05 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
From playlist MIT 3.320 Atomistic Computer Modeling of Materials
Fast and Memory Efficient Differentially Private-SGD via JL Projections
A Google TechTalk, presented by Sivakanth Gopi, 2021/05/21 ABSTRACT: Differential Privacy for ML Series. Differentially Private-SGD (DP-SGD) of Abadi et al. (2016) and its variations are the only known algorithms for private training of large scale neural networks. This algorithm requires
From playlist Differential Privacy for ML
Statistical Rethinking Winter 2019 Lecture 20
Lecture 20 of the Dec 2018 through March 2019 edition of Statistical Rethinking: A Bayesian Course with R and Stan. Covers Chapter 15, measurement error and missing data imputation.
From playlist Statistical Rethinking Winter 2019
Dan Crisan - Convergence of particle filters and relation to DA III
PROGRAM: Nonlinear filtering and data assimilation DATES: Wednesday 08 Jan, 2014 - Saturday 11 Jan, 2014 VENUE: ICTS-TIFR, IISc Campus, Bangalore LINK:http://www.icts.res.in/discussion_meeting/NFDA2014/ The applications of the framework of filtering theory to the problem of data assimi
From playlist Nonlinear filtering and data assimilation
Where does the statistics of complex systems come from? by Stefan Thurner
Program Summer Research Program on Dynamics of Complex Systems ORGANIZERS: Amit Apte, Soumitro Banerjee, Pranay Goel, Partha Guha, Neelima Gupte, Govindan Rangarajan and Somdatta Sinha DATE : 15 May 2019 to 12 July 2019 VENUE : Madhava hall for Summer School & Ramanujan hall f
From playlist Summer Research Program On Dynamics Of Complex Systems 2019
Monte Carlo methods and Optimization: Intertwinings (Lecture 1) by Gersende Fort
PROGRAM : ADVANCES IN APPLIED PROBABILITY ORGANIZERS : Vivek Borkar, Sandeep Juneja, Kavita Ramanan, Devavrat Shah and Piyush Srivastava DATE & TIME : 05 August 2019 to 17 August 2019 VENUE : Ramanujan Lecture Hall, ICTS Bangalore Applied probability has seen a revolutionary growth in r
From playlist Advances in Applied Probability 2019
JUDGMENT and SNOWBALL Non-random Sampling (12-6)
Judgment sampling (a.k.a., expert sampling, authoritative sampling, purposive sampling, judgmental sampling) is a technique in which the sample is selected based on the researcher’s (or other experts’) existing knowledge or professional judgment. It may provide highly accurate findings wit
From playlist Sampling Distributions in Statistics (WK 12 - QBA 237)
Random Matrix Theory And its Applications by Satya Majumdar ( Lecture - 1 )
PROGRAM BANGALORE SCHOOL ON STATISTICAL PHYSICS - X ORGANIZERS : Abhishek Dhar and Sanjib Sabhapandit DATE : 17 June 2019 to 28 June 2019 VENUE : Ramanujan Lecture Hall, ICTS Bangalore This advanced level school is the tenth in the series. This is a pedagogical school, aimed at bridgin
From playlist Bangalore School on Statistical Physics - X (2019)