Least squares

Iteratively reweighted least squares

The method of iteratively reweighted least squares (IRLS) is used to solve certain optimization problems with objective functions of the form of a p-norm: by an iterative method in which each step involves solving a weighted least squares problem of the form: IRLS is used to find the maximum likelihood estimates of a generalized linear model, and in robust regression to find an M-estimator, as a way of mitigating the influence of outliers in an otherwise normally-distributed data set. For example, by minimizing the least absolute errors rather than the least square errors. One of the advantages of IRLS over linear programming and convex programming is that it can be used with Gauss–Newton and Levenberg–Marquardt numerical algorithms. (Wikipedia).

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Comparing Iterative and Recursive Factorial Functions

Comparing iterative and recursive factorial functions

From playlist Computer Science

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The hidden beauty of the A* algorithm

00:00 Intro 01:38 Change the lengths! 06:34 What is a good potential? 12:31 Implementation 16:20 Bonus Video of Tom Sláma: https://youtu.be/umszOeerdsU Some related stuff: -- One thing I did not mention is that Dijkstra's algorithm is designed to solve the problem of finding the shortes

From playlist Fourier

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Lecture 3 – Word Vectors 2 | Stanford CS224U: Natural Language Understanding | Spring 2019

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai Professor Christopher Potts & Consulting Assistant Professor Bill MacCartney, Stanford University http://onlinehub.stanford.edu/ Professor Christopher Potts Pr

From playlist Stanford CS224U: Natural Language Understanding | Spring 2019

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Lecture 4 – Word Vectors 3 | Stanford CS224U: Natural Language Understanding | Spring 2019

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai Professor Christopher Potts Professor of Linguistics and, by courtesy, Computer Science Director, Stanford Center for the Study of Language and Information http:

From playlist Stanford CS224U: Natural Language Understanding | Spring 2019

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14. APSP and Johnson

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From playlist MIT 6.006 Introduction to Algorithms, Spring 2020

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TMCF workshop - Theory and methods challenges in counterfactual prediction, Karla Diaz-Ordaz

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From playlist Theory and Methods Challenge Fortnights

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Sylvia Biscoveanu - Power Spectral Density Uncertainty and Gravitational-Wave Parameter Estimation

Recorded 19 November 2021. Sylvia Biscoveanu of the Massachusetts Institute of Technology presents "The Effect of Power Spectral Density Uncertainty on Gravitational-Wave Parameter Estimation" at IPAM's Workshop III: Source inference and parameter estimation in Gravitational Wave Astronomy

From playlist Workshop: Source inference and parameter estimation in Gravitational Wave Astronomy

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Ik Siong Heng - Gaussian Mixture Models for transient gravitational wave detection - IPAM at UCLA

Recorded 29 November 2021. Ik Siong Heng of the University of Glasgow prsents "Gaussian Mixture Models for transient gravitational wave detection" at IPAM's Workshop IV: Big Data in Multi-Messenger Astrophysics. Abstract: The data from the gravitational wave detectors are non-stationary an

From playlist Workshop: Big Data in Multi-Messenger Astrophysics

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Lecture 10 - Decision Trees and Ensemble Methods | Stanford CS229: Machine Learning (Autumn 2018)

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3GdlrqJ Raphael Townshend PhD Candidate and CS229 Head TA To follow along with the course schedule and syllabus, visit: http://cs229.stanford.edu/syllabus-autumn

From playlist Stanford CS229: Machine Learning Full Course taught by Andrew Ng | Autumn 2018

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Determine a Least Squares Solutions to Ax=b

This video explains how to determine a least-squares solutions to Ax=b that has no solution.

From playlist Least Squares Solutions

Related pages

Restricted isometry property | Weighted least squares | Compressed sensing | Iterative method | Diagonal matrix | Generalized linear model | Lp space | Linear regression | M-estimator | Linear programming | Robust regression | Huber loss | Least squares | Geometric median | Regularization (mathematics)