Statistical hypothesis testing | Theory of probability distributions
In statistics, the monotone likelihood ratio property is a property of the ratio of two probability density functions (PDFs). Formally, distributions ƒ(x) and g(x) bear the property if that is, if the ratio is nondecreasing in the argument . If the functions are first-differentiable, the property may sometimes be stated For two distributions that satisfy the definition with respect to some argument x, we say they "have the MLRP in x." For a family of distributions that all satisfy the definition with respect to some statistic T(X), we say they "have the MLR in T(X)." (Wikipedia).
This is a short video tutorial on equivalent ratios. ✤ ✤ ✤ INTERACTIVE APPLETS AND WORKSHEETS ✤ ✤ ✤ http://fearlessmath.net ✤ ✤ ✤ FOLLOW ME ON TWITTER ✤ ✤ ✤ http://twitter.com/dhabecker
From playlist All about ratios and proportions
Calculate a Confidence Interval for a Population Proportion (Basic)
This example explains how to calculator a confidence interval for a population proportion.
From playlist Confidence Intervals
Please Subscribe here, thank you!!! https://goo.gl/JQ8Nys Definition of a Z-Score
From playlist Statistics
Kaggle Reading Group: On NMT Search Errors and Model Errors: Cat Got Your Tongue? | Kaggle
This week we'll be starting a new paper: "On NMT Search Errors and Model Errors: Cat Got Your Tongue?" by Felix Stahlber and Bill Byrne, published at EMNLP 2019. You can follow along with the paper here: https://www.aclweb.org/anthology/D19-1331.pdf About Kaggle: Kaggle is the world's lar
From playlist Kaggle Reading Group | Kaggle
This video defines a ratio and provides several examples on how to write a ratio and shows how to simplify a ratio. http://mathispower4u.wordpress.com/
From playlist Ratios and Rates
An example of z scores for proportions.
z scores, statistics, p values Like us on: http://www.facebook.com/PartyMoreStudyLess
From playlist z scores
Kyle Cranmer: "Deep Learning in the Physical Sciences"
New Deep Learning Techniques 2018 "Deep Learning in the Physical Sciences" Kyle Cranmer, New York University Abstract: The Physical Sciences are an interesting environment for deep learning techniques primarily because so much is known about the generative model for the data. Some field
From playlist New Deep Learning Techniques 2018
Saharon Rosset: Optimal and maximin procedures for multiple testing problems
CIRM VIRTUAL EVENT Recorded during the meeting "Mathematical Methods of Modern Statistics 2" the June 05, 2020 by the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Guillaume Hennenfent Find this video and other talks given by worldwide mathematicians
From playlist Virtual Conference
Sylvia Biscoveanu - Source characterization of individual compact binary coalescences - IPAM at UCLA
Recorded 20 September 2021. Sylvia Biscoveanu of the Massachusetts Institute of Technology presents "Source characterization of individual compact binary coalescences using Bayesian inference" at IPAM's Mathematical and Computational Challenges in the Era of Gravitational Wave Astronomy Tu
From playlist Tutorials: Math & Computational Challenges in the Era of Gravitational Wave Astronomy
http://AllSignalProcessing.com for more great signal-processing content: ad-free videos, concept/screenshot files, quizzes, MATLAB and data files. The likelihood ratio test maximizes the probability of correctly deciding hypothesis H_1 is true for any given probability of deciding H_0 is
From playlist Estimation and Detection Theory
Identifying and solving proportions
This is a short video tutorial on identifying and solving proportions. For interactive applets, worksheets, and more videos go to http://www.mathvillage.info
From playlist All about ratios and proportions
Tao Zou - Network Influence Analysis
Dr Tao Zou (ANU) presents "Network Influence Analysis”, 20 August 2020. Seminar organised by the Australian National University.
From playlist Statistics Across Campuses
Multi-armed Bandits Revisited by P R Kumar
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 resear
From playlist Advances in Applied Probability 2019
Three ways to solve a proportion
This is a short video tutorial on three ways to solve a proportion. For interactive applets, worksheets, and more videos go to http://www.mathvillage.info
From playlist All about ratios and proportions
Normal Distribution: Find Probability Using With Z-scores Using Tables
This lesson explains how to use tables to determine the probability a data value will have a z-score more than or less and a given z-score. It also shows how to determine the probability between two z-scores. Site: http://mathispower4u.com
From playlist The Normal Distribution
Stanford CS229: Machine Learning | Summer 2019 | Lecture 21 - Evaluation Metrics
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3b2QxDe Anand Avati Computer Science, PhD To follow along with the course schedule and syllabus, visit: http://cs229.stanford.edu/syllabus-summer2019.html 0:00
From playlist Stanford CS229: Machine Learning Course | Summer 2019 (Anand Avati)
Kyle Cranmer - Simulation-based Inference for Gravitational Wave Astronomy - IPAM at UCLA
Recorded 17 November 2021. Kyle Cranmer of New York University presents "Simulation-based Inference for Gravitational Wave Astronomy" at IPAM's Workshop III: Source inference and parameter estimation in Gravitational Wave Astronomy. Abstract: I will briefly review the taxonomy of simulatio
From playlist Workshop: Source inference and parameter estimation in Gravitational Wave Astronomy
Z Test of Proportion Using Calculator
Using a TI calculator to perform a Z Test on an unknown population proportion [a 1 Proportion Z test or 1Prop Z Test]
From playlist Unit 8: Hypothesis Tests & Confidence Intervals for Single Means & for Single Proportions
Towards Analyzing Normalizing Flows by Navin Goyal
Program Advances in Applied Probability II (ONLINE) ORGANIZERS Vivek S Borkar (IIT Bombay, India), Sandeep Juneja (TIFR Mumbai, India), Kavita Ramanan (Brown University, Rhode Island), Devavrat Shah (MIT, US) and Piyush Srivastava (TIFR Mumbai, India) DATE & TIME 04 January 2021 to 08 Janu
From playlist Advances in Applied Probability II (Online)
This lesson explains how to determine a z-score and how to find a z-score for a given data value. The percent of data above and below a data value and z-score is also found. Site: http://mathispower4u.com
From playlist The Normal Distribution