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
What is a Sampling Distribution?
Intro to sampling distributions. What is a sampling distribution? What is the mean of the sampling distribution of the mean? 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.creat
From playlist Probability Distributions
For more information on Bloom Filters, check the Wikipedias: http://en.wikipedia.org/wiki/Bloom_filter , for special topics like "How to get around the 'no deletion' rule" and "How do I generate all of these different hash functions anyways?" For other questions, like "who taught you how
From playlist Software Development Lectures
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)
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)
(ML 17.11) Rejection sampling - uniform case
From playlist Machine Learning
How to Choose a SAMPLING Method (12-7)
When possible, use probability sampling methods, such as simple random, stratified, cluster, or systematic sampling.
From playlist Sampling Distributions in Statistics (WK 12 - QBA 237)
How To Make Educational Game For Kids About Garbage Sorting In JS | Session 11 | #gamedev
Don’t forget to subscribe! This series is about how to make an educational game about garbage sorting in JS. In this tutorial, we will create a simple educational cross-platform game for kids using Javascript ecosystem capabilities. We will use extensive tools such as npm, gulp and bab
From playlist Make Educational Game For Kids About Garbage Sorting In JS
I discuss causal and non-causal noise filters: the moving average filter and the exponentially weighted moving average. I show how to do this filtering in Excel and Python
From playlist Discrete
Velocity 09: Chris Bissell, Jeremy Custenborder, and Yadid
Chris Bissell, Jeremy Custenborder, and Yadid Ramot (MySpace.com) "Fistful of Sand: Monitoring Code Performance at MySpace.com"
From playlist Velocity 2009
SICSS 2017 - Distributed Data Collection (Day 5. June 23, 2017)
The first Summer Institute in Computational Social Science was held at Princeton University from June 18 to July 1, 2017, sponsored by the Russell Sage Foundation. For more details, please visit https://compsocialscience.github.io/summer-institute/2017/
From playlist SICSS 2017 - Mass Collaboration (6/23)
ZuriHac2016: Parallelizing and Distributing Scientific Software in Haskell
A Google TechTalk, July 22, 2016, presented by Francesco Mazzoli ABSTRACT: Suppose you have a pure, CPU intensive function that you need to run on 10000 different inputs. This seems the dream task to parallelize in Haskell, but as it turns out it is not as easy as one might think. In this
From playlist ZuriHac 2016
Tim Harris: "Systems Challenges in Graph Analytics"
The Turing Lectures: Industrial & Commercial - Tim Harris – Oracle Laboratories: Systems Challenges in Graph Analytics Click the below timestamps to navigate the video. 00:00:10 Introduction by Professor Chris Williams, Edinburgh University 00:01:49 Tim Harris – Oracle Laboratories: Syst
From playlist Turing Lectures
Memory Management Masterclass with Addy Osmani
Addy is a senior engineer on the Chrome web engineering team, focusing on tools to help improve developer productivity and satisfaction. He works on Polymer - a Web Component library, is the the lead engineer on Yeoman and Web Starter Kit and regularly writes about web application architec
From playlist Development
LambdaConf 2015 - The Meaning of LFE Zeeshan Lakhani
Do you enjoy Lisp-based languages, built on s-expressions and homoiconicity? Do you like writing syntactic abstractions with pattern matching? What if you could use a Lisp to write a fault-tolerant, highly-available distributed datastore? Welcome to the wonderful world of LFE (Lisp-Flavore
From playlist LambdaConf 2015
Lec 10 | MIT 6.172 Performance Engineering of Software Systems, Fall 2010
Lecture 10: Dynamic Storage Allocation Instructor: Charles Leiserson View the complete course: http://ocw.mit.edu/6-172F10 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
From playlist MIT 6.172 Performance Engineering of Software Systems
6. Memory Don't Forget To Take Out The Garbage / Katie Fenn / ffconf 2017
JavaScript does a remarkable job of hiding memory management from us. What's going on behind the scenes?
From playlist ffconf 2017
Ruby Conf 12 - Simulating the World with Ruby by Bryan Liles
You want to model a world. That world has millions of people, who also interact with each other. How would you even start tackling this model in Ruby? I'd like to demonstrate one solution. In this talk, we'll explore this problem from inception. See how the process can evolve from the simp
From playlist Ruby Conference 2012
Let's Go Further: Build Concurrent Software using the Go Programming Language
Google Tech Talk April 25, 2012 Presented by Sameer Ajmani ABSTRACT Go is an open source programming environment that makes it easy to build simple, reliable, and efficient software. One of Go's key design goals is code adaptability; that it should be easy to take a simple design and bu
From playlist Google NYC Tech Talks
Distributions - Statistical Inference
In this video I talk about distribution, how to visualize it and also provide a concrete definition for it.
From playlist Statistical Inference