Home page for the book, "Bayesian Data Analysis"
This is the home page for the book, Bayesian Data Analysis, by Andrew Gelman, John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Donald Rubin. Here is the book in pdf form, available for download for non-commercial purposes. Teaching Bayesian data analysis Aki Vehtari's course material, including video lectures, slides, and his notes for most of the chapters. 77 best lines from my course A stu
BS14-06.pptx
• • • • • • • P H1 | D ( )= P D | H1 ( )P H1 ( ) P D∩Hi ( ) i=1 N ∑ = P D | H1 ( )P H1 ( ) P D | Hi ( )P Hi ( ) i=1 N ∑ = P D | H1 ( )P H1 ( ) P D ( ) P H | D ( )= P D | H ( )P H ( ) P D ( ) • • ( ) = ( ) ( ) ( ) ∠( ) ( ) • – – – • – – – • • • • • • • • • • • ( ) = ⋅ ⋅ − ( ) − ≈ ⋅ − ( ) − • ( ) = ⋅ ⋅ − ( ) − ∠⋅ − ( ) • • • ( ) ≈ ⋅ − ( ) − = α − ( ) β − ( ) α + β − ( ) α β ( ) = ⋅ α− ⋅ − ( ) β− <
Think Bayes
This HTML version of Think Bayes is provided for convenience, but it is not the best format for the book. In particular, some of the math symbols are not rendered correctly. You might prefer to read the PDF version, or you can buy a hard copy from Amazon. Think Bayes: Bayesian Statistics Made Simple Allen B. Downey Version 1.0.9 Copyright 2012 Allen B. Downey Permission is granted to copy, distrib
Think Bayes 2e – Green Tea Press
Bayesian Statistics Made Simple by Allen B. Downey You can order print and ebook versions of Think Bayes 2e from Bookshop.org and Amazon. You can also read Think Bayes 2e online (and follow the links there to the Jupyter notebooks). The code for this book is in this GitHub repository. Description Think Bayes is an introduction to Bayesian statistics using computational methods. The premise of this
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ã“ã®ãƒ–ãƒã‚°ã®èªè€…ã«ã¯ AIC (èµ¤æ± æƒ…å ±é‡åŸºæº–) ã‚’ã”å˜ã˜ã®æ–¹ã¯å¤šã„ã¨æ€ã„ã¾ã™. AIC ã¯çµ±è¨ˆãƒ¢ãƒ‡ãƒ«ã®è©•ä¾¡æŒ‡æ¨™ã¨ã—ã¦ä¸–ç•Œä¸ã§åºƒã使ã‚ã‚Œã¦ã„ã¾ã™ãŒã€ã“ã‚Œã¯èµ¤æ± 弘次ã¨ã„ã†æ—¥æœ¬äººçµ±è¨ˆå¦è€…ã«ã‚ˆã‚Šè€ƒæ¡ˆã•ã‚ŒãŸã‚‚ã®ã§ã™ã€‚ ã“ã‚Œã«å¯¾ã—ã€è¿‘å¹´ã€ãƒ™ã‚¤ã‚ºçµ±è¨ˆå¦ã§åˆ©ç”¨å¯èƒ½ãª WAIC ã¨ã„ã†æƒ…å ±é‡åŸºæº–ãŒè€ƒæ¡ˆã•ã‚Œã€ä¸–ç•Œä¸ã§çˆ†ç™ºçš„ã«æ™®åŠã—ã¦ã„ã¾ã™ã€‚ ã“ã® WAIC を考案ã—ãŸã®ã‚‚日本人ã§ã‚ã‚Šã€æ±å·¥å¤§ã®æ¸¡è¾ºæ¾„夫先生ã§ã™ã€‚ �L‚ÂŽg‚¦‚é�î•ñ—Ê‹K�€(WAIC) WAIC ã¯ã€ç®—出ã™ã‚‹ã“ã¨è‡ªä½“ã¯ç°¡å˜ãªã®ã§ã™ãŒã€ãã®ç†è«–çš„ãªæ ¹æ‹ ã¨ã—ã¦éžå¸¸ã«é«˜åº¦ãªæ•°å¦ãŒä½¿ã‚ã‚Œã¦ã„ã¾ã™ã€‚ ã“ã®ç†è«–ã«ã¤ã„ã¦ã€æ¸¡è¾ºå…ˆç”Ÿã”自身ãŒæ›¸ã‹ã‚ŒãŸæ›¸ç±ãŒã‚ã‚Šã¾ã™ã€‚ Algebraic Geometry and Statistical Learning Theory (Cambridge Monographs on Applied and Com
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ã¡ã‚ƒãŠãƒ»ãƒ»ãƒ»â€ 舞ã„é™ã‚Šãƒ»ãƒ»ãƒ»â€ 今回ã¯ã€Naive Bayesã¨Complement Naive Bayes, Negation Naive Bayesã§ã®å¤šã‚¯ãƒ©ã‚¹åˆ†é¡žã‚’比較ã—ãŸã„æ€ã„ã¾ã™ã€‚ãªãŠå¤šã‚¯ãƒ©ã‚¹åˆ†é¡žã¨ã¯ã€æ–‡æ›¸ ãŒã©ã®ã‚¯ãƒ©ã‚¹ ã«å±žã—ã¦ã„ã‚‹ã‹ã‚’求ã‚ã‚‹ã“ã¨ã§ã™ã€‚ ãã‚Œãžã‚Œã®æ‰‹æ³• Naive Bayes Naive Bayesã«ã‚ˆã‚‹å¤šã‚¯ãƒ©ã‚¹åˆ†é¡žã§ã¯ã€ãƒ™ã‚¤ã‚ºã®å®šç†ã‚’é©ç”¨ã—ãŸäº‹å¾Œç¢ºçŽ‡ ãŒæœ€å¤§ã¨ãªã‚‹ã‚¯ãƒ©ã‚¹ を求ã‚ã¾ã™ã€‚ãŸã 〠を直接推定ã™ã‚‹ã®ã¯é›£ã—ã„ã®ã§ã€æ–‡æ›¸ を文書ã«å«ã¾ã‚Œã‚‹å˜èªžåˆ— ã§è¿‘ä¼¼ã—ã¾ã™ã€‚ã¾ãŸã€è¨ˆç®—ã‚’ç°¡å˜ã«ã™ã‚‹ãŸã‚ã«å„クラスã§å˜èªžãŒç‹¬ç«‹ã«ç”Ÿèµ·ã™ã‚‹ã¨ä»®å®šã—ã¾ã™ã€‚ ãã—ã¦ã€æœ€çµ‚çš„ã«ä»¥ä¸‹ã®å¼ã«ã‚ˆã£ã¦æ–‡æ›¸ã®å±žã™ã‚‹ã‚¯ãƒ©ã‚¹ã‚’求ã‚ã¾ã™ã€‚ \begin{align} \tilde{c} = argmax_c P(c) \prod_{i=1}^{n} P(w_i|c) \end{align} Comp
尤度ã¨ã¯ä½•è€…ãªã®ã‹ï¼Ÿ - MyEnigma
ã¾ãšã¯ã“ã®ä¸€å†Šã‹ã‚‰ æ„味ãŒã‚ã‹ã‚‹ãƒ™ã‚¤ã‚ºçµ±è¨ˆå¦ (BERET SCIENCE) 目次 目次 ã¯ã˜ã‚㫠最尤推定法ã¨ãƒ™ã‚¤ã‚ºæŽ¨å®šã®é•ã„ 尤度をグラフィカルã«èª¬æ˜Žã™ã‚‹è³‡æ–™ å‚考資料 MyEnigma Supporters ã¯ã˜ã‚ã« å¦ç”Ÿã®æ™‚ã‹ã‚‰ï¼Œ "ãã‚Œã¯æœ€å°¤æŽ¨å®šæ³•ã‚’用ã„ã¦ã„ã¾ã™" ã¨ã‹ï¼Œ "ãã®è¡Œã¯ï¼Œå°¤åº¦è¨ˆç®—ã®éƒ¨åˆ†ã§ã™ã†ã¨ã‹ï¼Œã¾ã‚‹ã§å°¤åº¦ã¨ã„ã†ã‚‚ã®ã‚’知ã£ã¦ã„ã¦ï¼Œ 使ã„ã“ãªã—ã¦ã„ã‚‹ã‹ã®ã‚ˆã†ãªç™ºè¨€ã‚’ã—ã¦ãã¾ã—ãŸï¼Ž ã—ã‹ã—，ãã†è¨€ã„ãªãŒã‚‰ã‚‚， 自分的ã«ã¯ï¼Œå°¤åº¦ã¨ã„ã†ã‚‚ã®ãŒã‹ãªã‚Šæ¼ 然ã¨ã—ã¦ã„ã¾ã—ãŸï¼Ž ãã‚‚ãも，尤度ã¯æ–‡å—通り「尤もらã—ã•ã€ã‚’表ã™åº¦åˆã„ãªã®ã§ï¼Œ 「最尤推定法ã§ãƒ¢ãƒ‡ãƒ«ã®ãƒ‘ラメータを決定ã—ã¾ã™ï¼Žã€ ã£ã¦ã„ã†ã®ã¯ï¼Œ 「一番ãã‚Œã£ã½ã„ã‚‚ã®ã‚’é¸ã³ã¾ã™ã€ ã¨è¨€ã£ã¦ã„ã‚‹ã®ã¨ã‚ã¾ã‚Šå¤‰ã‚ã‚ŠãŒãªã„æ°—ãŒã—ã¦ã„ãŸã®ã§ã™(笑) 最近，ã“ã®æœ¬ã‚’èªã‚“ã§æœ€é©åŒ–を一ã‹ã‚‰ä½“系的ã«å¦ã³ç›´ã—ã¦ã„ã‚‹ã®ã§ã™ãŒï¼Œ ã‚„ã£ã¨ï¼Œå°¤åº¦ã¨ã„ã†
心ç†å¦è€…ã®ãŸã‚ã®ãƒ™ã‚¤ã‚ºçµ±è¨ˆå…¥é–€ | Sunny side up!
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https://scikit-learn.org/stable/modules/naive_bayes.html
http://www.singularpoint.org/blog/r/normal-posterior-inverse-wishart/
http://truestar.jp/main/metrics/basicstatistics/Bayes%20Basic.pdf
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