Causal Inference: What If (the book)
Jamie Robins and I have written a book that provides a cohesive presentation of concepts of, and methods for, causal inference. Much of this material is currently scattered across journals in several disciplines or confined to technical articles. We expect that the book will be of interest to anyone interested in causal inference, e.g., epidemiologists, statisticians, psychologists, economists, so
Computer Vision: Algorithms and Applications, 2nd ed.
Computer Vision: Algorithms and Applications, 2nd ed. © 2022 Richard Szeliski, The University of Washington Welcome to the website (https://szeliski.org/Book) for the second edition of my computer vision textbook, which is now available for purchase at Amazon, Springer, and other booksellers. To download an electronic version of the book, please fill in your information on this page. You are welco
Computer Vision: Models, Learning, and Inference
Computer Vision: Models, Learning, and Inference Simon J.D. Prince A new machine vision textbook with 600 pages, 359 colour figures, 201 exercises and 1060 associated Powerpoint slides Published by Cambridge University Press NOW AVAILABLE from Amazon and other booksellers. "Simon Prince’s wonderful book presents a principled model-based approach to computer vision that unifies disparate algorithm
Gaussian Processes for Machine Learning: Book webpage
Carl Edward Rasmussen and Christopher K. I. Williams The MIT Press, 2006. ISBN 0-262-18253-X. Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical
Gaussian Processes for Machine Learning: Contents
Carl Edward Rasmussen and Christopher K. I. Williams MIT Press, 2006. ISBN-10 0-262-18253-X, ISBN-13 978-0-262-18253-9. This book is © Copyright 2006 by Massachusetts Institute of Technology. The MIT Press have kindly agreed to allow us to make the book available on the web. The web version of the book corresponds to the 2nd printing. You can buy the book for a list price of 50.00 US$ or 40.00 UK£
統計ライブラリー マルコフ連鎖モンテカルロ法 ｜朝倉書店
ベイズ統計の発展で重要性が高まるMCMC法を応用例を多数示しつつ徹底解説。Ｒソース付〔内容〕MCMC法入門／母数推定／収束判定・モデルの妥当性／SEMによるベイズ推定／MCMC法の応用／BRugs／ベイズ推定の古典的枠組み。 1. マルコフ連鎖モンテカルロ法入門 1.1 マルコフ連鎖 1.2 モンテカルロ法 1.3 マルコフ連鎖モンテカルロ法 2. MCMCによる母数推定の実際 2.1 ベイズ推測におけるMCMCの利用 2.2 ギブスサンプラーによる回帰モデルの推定 2.3 複合MCMCによる項目反応モデルの推定 3. 収束判定およびモデルの妥当性の検討 3.1 収束判定のための方法 3.2 モデルの良さを検討するための方法 4. SEMにおけるベイズ推定 4.1 一般的なSEMのベイズ推定 4.2 順序カテゴリカルSEMのベイズ推定 4.3 潜在混合モデリング 4.4 欠測データのあるS
Machine Learning in Action
Machine Learning in Action is unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification. about the
Stephen Marsland
This webpage contains the code and other supporting material for the textbook "Machine Learning: An Algorithmic Perspective" by Stephen Marsland, published by CRC Press, part of the Taylor and Francis group. The first edition was published in 2009, and a revised and updated second edition is due out towards the end of 2014. The book is aimed at computer science and engineering undergraduates studi
Introduction to Information Retrieval
This is the companion website for the following book. Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, Introduction to Information Retrieval, Cambridge University Press. 2008. You can order this book at CUP, at your local bookstore or on the internet. The best search term to use is the ISBN: 0521865719. The book aims to provide a modern approach to information retrieval from a compu
Project Gutenberg - free ebooks
This page is no longer maintained and may contain outdated information. New Website 2020 On August 26 2020, the Project Gutenberg website underwent some major changes. These changes had been previewed since early 2020, and visitors to the old site were invited to try the new site, including giving input via a brief survey. The old site is no longer available. If you found yourself on this page une
BLOG::broomie.net: 機械学習・自然言語処理のリソースリンク集
いつもネット上で機械学習やNLPに関する便利そうなツールや，有用なドキュメント，動画，ツールなどをメモしているのですが，今後の調査のためにどこかに一つに集約しておきたいなあ，と思い，この記事に集約しておくことにしました．昨今，自動リンク集や，自動インデックス作成に関して研究が盛んにされていますが，これは人力リンク集です！リンク先はほとんどさらっとしか見ていないので本当に有用かどうか保証できませんが，興味を持ったものは掘り下げて別の記事で紹介したいと思います．おもしろいものを見つけしだい，このエントリーは更新していきたいと思います． ※ 有名どころをカバーしているわけではありません，あくまで気まぐれで追加していきます．．． ※ いくつかカテゴリ間で重複します 解説・読み物系リソース 全般 Statistical Data Mining Tutorials Tutorial Slides by
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Machine learning textbook
Machine learning textbook
Bayesian Computation with R
Bayesian Inference
Amazon.co.jp: Rで学ぶベイズ統計学入門: J. アルバート (著), 基広,石田 (翻訳), 和枝,石田 (翻訳), Albert,Jim (原名): 本
Mahout in Action
Elements of Statistical Learning: data mining, inference, and prediction. 2nd Edition.
青空文庫 Aozora Bunko
Amazon.co.jp: Mining the Web: Discovering Knowledge from Hypertext Data (The Morgan Kaufmann Series in Data Management Systems): Chakrabarti, Soumen: 本
