Inverse probability weighting is a statistical technique for calculating statistics standardized to a pseudo-population different from that in which the data was collected. Study designs with a disparate sampling population and population of target inference (target population) are common in application. There may be prohibitive factors barring researchers from directly sampling from the target population such as cost, time, or ethical concerns. A solution to this problem is to use an alternate design strategy, e.g. stratified sampling. Weighting, when correctly applied, can potentially improve the efficiency and reduce the bias of unweighted estimators. One very early weighted estimator is the Horvitz–Thompson estimator of the mean. When the sampling probability is known, from which the sampling population is drawn from the target population, then the inverse of this probability is used to weight the observations. This approach has been generalized to many aspects of statistics under various frameworks. In particular, there are weighted likelihoods, weighted estimating equations, and weighted probability densities from which a majority of statistics are derived. These applications codified the theory of other statistics and estimators such as marginal structural models, the standardized mortality ratio, and the EM algorithm for coarsened or aggregate data. Inverse probability weighting is also used to account for missing data when subjects with missing data cannot be included in the primary analysis.With an estimate of the sampling probability, or the probability that the factor would be measured in another measurement, inverse probability weighting can be used to inflate the weight for subjects who are under-represented due to a large degree of missing data. (Wikipedia).
Ex: Find the Inverse of a 2x2 Matrix Using a Formula
This video explains how to find the inverse of a 2x2 matrix using the inverse formula. Site: http://mathispower4u.com
From playlist Inverse Matrices
Ex 1: Find the Inverse of a Function
This video provides two examples of how to determine the inverse function of a one-to-one function. A graph is used to verify the inverse function was found correctly. Library: http://mathispower4u.com Search: http://mathispower4u.wordpress.com
From playlist Determining Inverse Functions
Ex 2: Find the Inverse of a Function
This video provides two examples of how to determine the inverse function of a one-to-one function. A graph is used to verify the inverse function was found correctly. Library: http://mathispower4u.com Search: http://mathispower4u.wordpress.com
From playlist Determining Inverse Functions
👉 Learn how to find the inverse of a linear function. A linear function is a function whose highest exponent in the variable(s) is 1. The inverse of a function is a function that reverses the "effect" of the original function. One important property of the inverse of a function is that whe
From playlist Find the Inverse of a Function
Use the inverse of a function to determine the domain and range
👉 Learn how to find the inverse of a linear function. A linear function is a function whose highest exponent in the variable(s) is 1. The inverse of a function is a function that reverses the "effect" of the original function. One important property of the inverse of a function is that whe
From playlist Find the Inverse of a Function
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From playlist Find the Inverse of a Function
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👉 Learn how to find the inverse of a linear function. A linear function is a function whose highest exponent in the variable(s) is 1. The inverse of a function is a function that reverses the "effect" of the original function. One important property of the inverse of a function is that whe
From playlist Find the Inverse of a Function
Leonid Petrov (Virginia) -- Random polymers and symmetric functions
I will survey integrable random polymers (based on gamma / inverse gamma or beta distributed weights), and explain their connection to symmetric functions (respectively, gl_n Whittaker and new spin Whittaker functions). The work on spin Whittaker functions is joint with Matteo Mucciconi.
From playlist Integrable Probability Working Group
Lec 15 | MIT 18.085 Computational Science and Engineering I
Numerical methods in estimation: recursive least squares and covariance matrix A more recent version of this course is available at: http://ocw.mit.edu/18-085f08 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
From playlist MIT 18.085 Computational Science & Engineering I, Fall 2007
Probabilistic inverse problems (Lecture - 2) by Erkki Somersalo
DISCUSSION MEETING WORKSHOP ON INVERSE PROBLEMS AND RELATED TOPICS (ONLINE) ORGANIZERS: Rakesh (University of Delaware, USA) and Venkateswaran P Krishnan (TIFR-CAM, India) DATE: 25 October 2021 to 29 October 2021 VENUE: Online This week-long program will consist of several lectures by
From playlist Workshop on Inverse Problems and Related Topics (Online)
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From playlist Intermediate Algebra (Full Length Videos)
Michael Damron (Georgia Tech) -- Critical first-passage percolation in two dimensions
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From playlist Columbia Probability Seminar
Xavier Viennot: Heaps and lattice paths
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From playlist Combinatorics
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MIT 18.650 Statistics for Applications, Fall 2016 View the complete course: http://ocw.mit.edu/18-650F16 Instructor: Philippe Rigollet In this lecture, Prof. Rigollet talked about Hessian, Fisher information, weighted least squares, and iteratively reweighed least squares. License: Creat
From playlist MIT 18.650 Statistics for Applications, Fall 2016
Geoffrey Hinton and his co-authors describe a biologically plausible variant of backpropagation and report evidence that such an algorithm might be responsible for learning in the brain. https://www.nature.com/articles/s41583-020-0277-3 Abstract: During learning, the brain modifies synap
From playlist General Machine Learning
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Talk by Benjamin Steinberg in Global Noncommutative Geometry Seminar (Americas), https://globalncgseminar.org/talks/tba-15/ on Oct. 8, 2021
From playlist Global Noncommutative Geometry Seminar (Americas)
Finding the inverse of a function
👉 Learn how to find the inverse of a linear function. A linear function is a function whose highest exponent in the variable(s) is 1. The inverse of a function is a function that reverses the "effect" of the original function. One important property of the inverse of a function is that whe
From playlist Find the Inverse of a Function
Quantum Groups Seminar Topic: R-matrices Speaker: Elijah Bodish Affiliation: University of Oregon Date: February 18, 2021 For more video please visit http://video.ias.edu
From playlist Quantum Groups Seminar