Mathematical modeling | Estimation methods | Statistical distance

Minimum-distance estimation

Minimum-distance estimation (MDE) is a conceptual method for fitting a statistical model to data, usually the empirical distribution. Often-used estimators such as ordinary least squares can be thought of as special cases of minimum-distance estimation. While consistent and asymptotically normal, minimum-distance estimators are generally not statistically efficient when compared to maximum likelihood estimators, because they omit the Jacobian usually present in the likelihood function. This, however, substantially reduces the computational complexity of the optimization problem. (Wikipedia).

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Finding Minimum Distance

In this video I briefly explain what minimum distance is and why it is helpful. Then I explain how to find it "the long way" and the "shortcut." Also during the process, I explain what Hamming Weight and Distance are and how to find them. Codewords from Generating Matrix Video: https://w

From playlist Cryptography and Coding Theory

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Maximum and Minimum Values (Closed interval method)

A review of techniques for finding local and absolute extremes, including an application of the closed interval method

From playlist 241Fall13Ex3

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The Distance Formula

This video show how to use the distance formula to determine the distance between two points. It also shows how it is derived from the Pythagorean theorem. http://mathispower4u.yolasite.com/

From playlist Using the Distance Formula / Midpoint Formula

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Using Bounds to Calculate Further Bounds

"Use lower and upper bounds within calculations to calculate a further lower/upper bound."

From playlist Number: Rounding & Estimation

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Example: Determine the Distance Between Two Points

This video shows an example of determining the length of a segment on the coordinate plane by using the distance formula. Complete Video List: http://www.mathispower4u.yolasite.com or http://www.mathispower4u.wordpress.com

From playlist Using the Distance Formula / Midpoint Formula

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Pre-Calculus - Identify the local maximum and minimum of a function

This video shows how to find the local maximum and minimum points when looking at the graph of a function. Remember that these are the maximum and minimum on some interval of the entire function. More specific techniques are covered for other functions like quadratics in later videos. F

From playlist Pre-Calculus

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How to Make 3D Fractals

This is a brief introduction to how ray marching works and how it is used to generate amazing real-time 3D fractals. I uploaded the source code so you can give it a try yourself! Source code: https://github.com/HackerPoet/PySpace Ray marching tutorial: http://blog.hvidtfeldts.net/index.

From playlist Fractals & Math

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11. Weighted Shortest Paths

MIT 6.006 Introduction to Algorithms, Spring 2020 Instructor: Jason Ku View the complete course: https://ocw.mit.edu/6-006S20 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP63EdVPNLG3ToM6LaEUuStEY This lecture discusses weighted graphs and weighted paths. This prepares

From playlist MIT 6.006 Introduction to Algorithms, Spring 2020

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Lec 24 | MIT 18.086 Mathematical Methods for Engineers II

Error Estimates / Projections View the complete course at: http://ocw.mit.edu/18-086S06 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu

From playlist MIT 18.086 Mathematical Methods for Engineers II, Spring '06

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Estimation, Distances, and Smoothness (3Blue1Brown Summer of Math Exposition)

To make estimates in new situations, we typically draw upon similar experiences from memory. In this video, I try to explain the mathematics behind such reasoning. This is a submission to the 3blue1brown summer of math exposition, 2021. https://www.3blue1brown.com/blog/some1 Introduction

From playlist Summer of Math Exposition Youtube Videos

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Charles Bordenave: Compter et optimiser avec les graphes unimodulaires - 1

Find this video and other talks given by worldwide mathematicians on CIRM's Audiovisual Mathematics Library: http://library.cirm-math.fr. And discover all its functionalities: - Chapter markers and keywords to watch the parts of your choice in the video - Videos enriched with abstracts, b

From playlist Probability and Statistics

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Lec 5 | MIT 6.451 Principles of Digital Communication II

Introduction to Binary Block Codes View the complete course: http://ocw.mit.edu/6-451S05 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu

From playlist MIT 6.451 Principles of Digital Communication II

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Lec 17 | MIT 6.046J / 18.410J Introduction to Algorithms (SMA 5503), Fall 2005

Lecture 17: Shortest Paths I: Properties, Dijkstra's Algorithm, Breadth-first Search View the complete course at: http://ocw.mit.edu/6-046JF05 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu

From playlist MIT 6.046J / 18.410J Introduction to Algorithms (SMA 5503),

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13. Dijkstra

MIT 6.006 Introduction to Algorithms, Spring 2020 Instructor: Jason Ku View the complete course: https://ocw.mit.edu/6-006S20 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP63EdVPNLG3ToM6LaEUuStEY This class discusses a fourth weighted single-source shortest path algor

From playlist MIT 6.006 Introduction to Algorithms, Spring 2020

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Lecture 14 - Support Vector Machines

Support Vector Machines - One of the most successful learning algorithms; getting a complex model at the price of a simple one. Lecture 14 of 18 of Caltech's Machine Learning Course - CS 156 by Professor Yaser Abu-Mostafa. View course materials in iTunes U Course App - https://itunes.apple

From playlist Machine Learning Course - CS 156

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Véronique Maume-Deschamps: On the estimation of conditional quantiles I

VIRTUAL LECTURE Recording during the meeting "Quasi-Monte Carlo Methods and Applications" Find this video and other talks given by worldwide mathematicians on CIRM's Audiovisual Mathematics Library: http://library.cirm-math.fr. And discover all its functionalities: - Chapter markers an

From playlist Virtual Conference

Related pages

Maximum spacing estimation | Jacobian matrix and determinant | Cramér–von Mises criterion | Distance | Cumulative distribution function | Statistical population | Estimator | Special case | Functional (mathematics) | Anderson–Darling test | Kolmogorov–Smirnov test | Computational complexity | Asymptotic distribution | Empirical distribution function | Maximum likelihood estimation | Goodness of fit | Ordinary least squares | Likelihood function | Dependent and independent variables | Random variable | Efficient estimator | Consistent estimator | Absolute difference