Statistics
The study of the collection, analysis, interpretation, presentation, and organization of data.
All categoriesBooks under this sub-category (50 books)
The Elements of Statistical Learning: Data Mining, Inference, and Prediction.
Post date: 09 Apr 2016This book descibes the important ideas of data mining, machine learning, and bioinformatics in a common conceptual framework. Topics include neural networks, support vector machines, classification trees and boosting.
The Elements of Statistical Learning: Data Mining, Inference, and Prediction.
Post date: 09 Apr 2016This book descibes the important ideas of data mining, machine learning, and bioinformatics in a common conceptual framework. Topics include neural networks, support vector machines, classification trees and boosting.
The Probability and Statistics Cookbook
Post date: 14 May 2016This cookbook is a succinct representation of various topics in probability theory and statistics. It provides a comprehensive mathematical reference reduced to its essence, rather than aiming for elaborate explanations.
Publication date: 12 Mar 2016
License: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International
License: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International
The Probability and Statistics Cookbook
Post date: 14 May 2016This cookbook is a succinct representation of various topics in probability theory and statistics. It provides a comprehensive mathematical reference reduced to its essence, rather than aiming for elaborate explanations.
Publication date: 12 Mar 2016
License: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International
License: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International
Post date: 23 Jun 2017
This document originated from lecture notes of mathematical statistics course taught at George Mason University. Students should be able to work through the details of "hard" proofs and derivations.
This document originated from lecture notes of mathematical statistics course taught at George Mason University. Students should be able to work through the details of "hard" proofs and derivations.
Post date: 23 Jun 2017This document originated from lecture notes of mathematical statistics course taught at George Mason University. Students should be able to work through the details of "hard" proofs and derivations.
Think Bayes: Bayesian Statistics in Python
Post date: 02 Apr 2016An introduction to Bayesian statistics using simple Python programs instead of complicated math.
Publisher:
O’Reilly Media, Inc.
Publication date: 04 Oct 2013
License: Creative Commons Attribution-NonCommercial 3.0 Unported
Publication date: 04 Oct 2013
License: Creative Commons Attribution-NonCommercial 3.0 Unported
Think Bayes: Bayesian Statistics in Python
Post date: 02 Apr 2016An introduction to Bayesian statistics using simple Python programs instead of complicated math.
Publisher:
O’Reilly Media, Inc.
Publication date: 04 Oct 2013
License: Creative Commons Attribution-NonCommercial 3.0 Unported
Publication date: 04 Oct 2013
License: Creative Commons Attribution-NonCommercial 3.0 Unported
Think Stats: Probability and Statistics for Programmers
Post date: 02 Apr 2016An introduction to Probability and Statistics for Python programmers.
Publisher:
O’Reilly Media, Inc.
Publication date: 27 Oct 2014
License: Creative Commons Attribution-NonCommercial 3.0 Unported
Publication date: 27 Oct 2014
License: Creative Commons Attribution-NonCommercial 3.0 Unported
Think Stats: Probability and Statistics for Programmers
Post date: 02 Apr 2016An introduction to Probability and Statistics for Python programmers.
Publisher:
O’Reilly Media, Inc.
Publication date: 27 Oct 2014
License: Creative Commons Attribution-NonCommercial 3.0 Unported
Publication date: 27 Oct 2014
License: Creative Commons Attribution-NonCommercial 3.0 Unported
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