ARGUS distribution
In physics, the ARGUS distribution, named after the particle physics experiment ARGUS, is the probability distribution of the reconstructed invariant mass of a decayed particle candidate in continuum background. (Wikipedia).
In physics, the ARGUS distribution, named after the particle physics experiment ARGUS, is the probability distribution of the reconstructed invariant mass of a decayed particle candidate in continuum background. (Wikipedia).
The Normal Distribution (1 of 3: Introductory definition)
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From playlist The Normal Distribution
Excel 2013 Statistical Analysis #09: Cumulative Frequency Distribution & Chart, PivotTable & Formula
Download files: https://people.highline.edu/mgirvin/AllClasses/210Excel2013/Ch02/Excel2013StatisticsChapter02.xlsx Topics in this video: 1. (00:09) Overview of % Cumulative Frequency 2. (00:42) Formulas to create Cumulative Frequency Distribution and % Cumulative Frequency Distribution. 3.
From playlist Excel for Statistical Analysis in Business & Economics Free Course at YouTube (75 Videos)
Statistics Lecture 6.3: The Standard Normal Distribution. Using z-score, Standard Score
https://www.patreon.com/ProfessorLeonard Statistics Lecture 6.3: Applications of the Standard Normal Distribution. Using z-score, Standard Score
From playlist Statistics (Full Length Videos)
Mean of Grouped Frequency Tables
"Calculate mean from grouped frequency tables."
From playlist Data Handling: Frequency Tables
What is the t-distribution? An extensive guide!
See all my videos at http://www.zstatistics.com/videos/ 0:00 Introduction 2:17 Overview 6:06 Sampling RECAP 12:27 Visualising the t distribution 14:24 Calculating values from the t distribution (EXCEL and t-tables!)
From playlist Distributions (10 videos)
Determining values of a variable at a particular percentile in a normal distribution
From playlist Unit 2: Normal Distributions
Introduction to the Standard Normal Distribution
This video introduces the standard normal distribution http://mathispower4u.com
From playlist The Normal Distribution
A polynomial lower bound for monotonicity testing...- Rocco Servedio
Rocco Servedio Columbia University March 31, 2014 We prove a Ω̃ (n1/5)Ω~(n1/5) lower bound on the query complexity of any non-adaptive two-sided error algorithm for testing whether an unknown n-variable Boolean function is monotone versus constant-far from monotone. This gives an exponenti
From playlist Mathematics
Gaussian multiplicative chaos: applications and recent developments - Nina Holden
50 Years of Number Theory and Random Matrix Theory Conference Topic: Gaussian multiplicative chaos: applications and recent developments Speaker: Nina Holden Affiliation: ETH Zurich Date: June 22, 2022 I will give an introduction to Gaussian multiplicative chaos and some of its applicati
From playlist Mathematics
L22.8 The Fresh Start Property and Its Implications
MIT RES.6-012 Introduction to Probability, Spring 2018 View the complete course: https://ocw.mit.edu/RES-6-012S18 Instructor: John Tsitsiklis License: Creative Commons BY-NC-SA More information at https://ocw.mit.edu/terms More courses at https://ocw.mit.edu
From playlist MIT RES.6-012 Introduction to Probability, Spring 2018
Mod-01 Lec-19 Ricardo-Malthus debate
History of Economic Theory by Dr. Shivakumar, Department of Humanities and Social Sciences IIT Madras, For more details on NPTEL visit http://nptel.iitm.ac.in
From playlist IIT Madras: History of Economic Theory | CosmoLearning.org Economics
Is Google Translate Sexist? Gender Stereotypes in Statistical Machine Translation
#genderbias #algorithmicfairness #debiasing A brief look into gender stereotypes in Google Translate. The origin is a Tweet containing a Hungarian text. Hungarian is a gender-neutral language, so translating gender pronouns is ambiguous. Turns out that Google Translate assigns very stereo
From playlist ML in Society
Discriminating Systems - Gender, Race, and Power in AI
TL;DR: - There exists both an unequal representation of people in the AI workforce as well as examples of societal bias in AI systems. - The authors claim that the former causally leads to the latter and vice versa. - To me, the report does not manage to make a strong enough argument for t
From playlist ML in Society
AI DEBATE : Yoshua Bengio | Gary Marcus
Yoshua Bengio and Gary Marcus on the best way forward for AI Moderated by Vincent Boucher ORIGINAL LIVE STREAMING | Monday, 23 December 2019 from 6:30 PM to 8:30 PM (EST) at Mila: https://www.facebook.com/MontrealAI/videos/498403850881660/ Transcript of the AI Debate: https://medium.com/
From playlist AI talks
AQC 2016 - Quantum Monte Carlo vs Tunneling vs. Adiabatic Optimization
A Google TechTalk, June 27, 2016, presented by Aram Harrow (MIT) ABSTRACT: Can quantum adiabatic evolution solve optimization problems much faster than classical computers? One piece of evidence for this has been their apparent advantage in "tunneling" through barriers to escape local mi
From playlist Adiabatic Quantum Computing Conference 2016
Stochastic Mechanisms of Cell-Size Regulation in Bacteria by Anatoly Kolomeisky
PROGRAM STATISTICAL BIOLOGICAL PHYSICS: FROM SINGLE MOLECULE TO CELL (ONLINE) ORGANIZERS: Debashish Chowdhury (IIT Kanpur), Ambarish Kunwar (IIT Bombay) and Prabal K Maiti (IISc, Bengaluru) DATE: 07 December 2020 to 18 December 2020 VENUE: Online 'Fluctuation-and-noise' are themes tha
From playlist Statistical Biological Physics: From Single Molecule to Cell (Online)
Ben Glocker: "Causality matters in medical imaging"
Deep Learning and Medical Applications 2020 "Causality matters in medical imaging" Ben Glocker - Imperial College London, Department of Computing Abstract: We use causal reasoning to shed new light on key challenges in medical imaging: 1) data scarcity, which is the limited availability
From playlist Deep Learning and Medical Applications 2020
(ML 7.7.A1) Dirichlet distribution
Definition of the Dirichlet distribution, what it looks like, intuition for what the parameters control, and some statistics: mean, mode, and variance.
From playlist Machine Learning