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Full-Text Articles in Statistical Theory
How Machine Learning And Probability Concepts Can Improve Nba Player Evaluation, Harrison Miller
How Machine Learning And Probability Concepts Can Improve Nba Player Evaluation, Harrison Miller
CMC Senior Theses
In this paper I will be breaking down a scholarly article, written by Sameer K. Deshpande and Shane T. Jensen, that proposed a new method to evaluate NBA players. The NBA is the highest level professional basketball league in America and stands for the National Basketball Association. They proposed to build a model that would result in how NBA players impact their teams chances of winning a game, using machine learning and probability concepts. I preface that by diving into these concepts and their mathematical backgrounds. These concepts include building a linear model using ordinary least squares method, the bias …
A New Approximation Scheme For Monte Carlo Applications, Bo Jones
A New Approximation Scheme For Monte Carlo Applications, Bo Jones
CMC Senior Theses
Approximation algorithms employing Monte Carlo methods, across application domains, often require as a subroutine the estimation of the mean of a random variable with support on [0,1]. One wishes to estimate this mean to within a user-specified error, using as few samples from the simulated distribution as possible. In the case that the mean being estimated is small, one is then interested in controlling the relative error of the estimate. We introduce a new (epsilon, delta) relative error approximation scheme for [0,1] random variables and provide a comparison of this algorithm's performance to that of an existing approximation scheme, both …