[B! Statistics] gologo13ã®ãƒ–ãƒƒã‚¯ãƒžãƒ¼ã‚¯

gologo13 id:gologo13

Statisticsã«é–¢ã™ã‚‹gologo13ã®ãƒ–ãƒƒã‚¯ãƒžãƒ¼ã‚¯ (34)

${{author_name}}$

{{{comment_expanded}}}

{{label}}

{{#is_bookmark}}ãƒªã‚¹ãƒˆ{{/is_bookmark}}{{^is_bookmark}}ãƒªãƒ³ã‚¯{{/is_bookmark}}

${{author_name}}$
{{author_name}}{{created}}
{{ #comment }}{{ comment }}{{ /comment }}
- {{ label }}

${{author_name}}$

{{{comment_expanded}}}

{{label}}

{{#is_bookmark}}ãƒªã‚¹ãƒˆ{{/is_bookmark}}{{^is_bookmark}}ãƒªãƒ³ã‚¯{{/is_bookmark}}

ã€Œæ¯Žæ—¥ã®æ•°å—ã‚’è¿½ã„ã‹ã‘ã€æ¯Žæ—¥æ”¹å–„ã™ã‚‹ã€ã“ã¨ã®æ„å¤–ãªè½ã¨ã—ç©´ - æ¸‹è°·é§…å‰ã§åƒããƒ‡ãƒ¼ã‚¿ã‚µã‚¤ã‚¨ãƒ³ãƒ†ã‚£ã‚¹ãƒˆã®ãƒ–ãƒã‚°
ï¼ˆâ€»ä»Šå›žã®è¨˜äº‹ã®å†…å®¹ã¯ã‹ãªã‚Šé›£è§£ã‹ã‚‚ã§ã™ï¼‰ å¤§ç«¹æ–‡é›„ã®çµŒæ¸ˆè„³ã‚’é›ãˆã‚‹ï¼ˆ2æœˆ13æ—¥åˆ†è¨˜äº‹ï¼‰ å¹¾ä½•ãƒ–ãƒ©ã‚¦ãƒ³é‹å‹•ã¨è¦‹ã›ã‹ã‘ã®å›žå¸° - My Life as a Mock Quant å¾—ã¦ã—ã¦å¤šãã®ä¼æ¥ã§ã¯ã€ã€Œæ¯Žæ—¥ã®æ•°å—ï¼ˆå£²ä¸Šé«˜ãƒ»åˆ©ç›Šãƒ»åœ¨åº«etc.ï¼‰ã‚’è¿½ã„ã‹ã‘ã€ã€ã€Œãã®çµæžœã‚’å…ƒæ‰‹ã«æ¯Žæ—¥æ”¹å–„ã™ã‚‹ã€ã¨ã„ã†ã“ã¨ã‚’æ—¥ã€…åŠ±è¡Œã—ã¦ã„ã‚‹ã®ã§ã¯ãªã„ã‹ã¨æ€ã„ã¾ã™ã€‚ ã¨ã“ã‚ã§ã€ã“ã‚“ãªä½“é¨“ã—ãŸã“ã¨ã¯ã‚ã‚Šã¾ã›ã‚“ã‹ï¼Ÿ ã€Œæ¯Žæ—¥æ¯Žæ—¥ã€ç‰©å‡„ãä¸€ç”Ÿæ‡¸å‘½æ•°å—ã‚’è¦‹ãªãŒã‚‰é ‘å¼µã£ã¦ã‚«ã‚¤ã‚¼ãƒ³ã—ç¶šã‘ã¦ã€ç¢ºã‹ã«é ‘å¼µã£ãŸæ™‚ã¯æ•°å—ã¯ä¸ŠãŒã£ãŸã—ã€é ‘å¼µã‚ŠãŒè¶³ã‚Šãªã„æ™‚ã¯æ•°å—ãŒä¸‹ãŒã£ãŸã€‚ãã‚Œã«ä¸€å–œä¸€æ†‚ã—ã¤ã¤ã‚‚ãšã£ã¨ç‰©å‡„ãé ‘å¼µã‚Šç¶šã‘ãŸãƒ»ãƒ»ãƒ»ã§ã‚‚ã€ã‚ã‚Œã‹ã‚‰ã‚‚ã†æ•°ãƒ¶æœˆçµŒã£ãŸã®ã«å…¨ä½“ã¨ã—ã¦ã¯æ•°å—ã¯ä¸‹ãŒã£ã¦ãã¦ã„ã‚‹ã€‚ã©ã†ã—ã¦ãªã‚“ã ã‚ã†ï¼Ÿï¼Ÿï¼Ÿã€ ãªã‚‹ã»ã©ã€ã‚‚ã—ã‹ã—ãŸã‚‰ãã®æ™‚ã®æ”¹å–„åŠªåŠ›ãŒæ£ã—ããªã‹ã£ãŸã®ã‹ã‚‚ã—ã‚Œã¾ã›ã‚“ã€‚ã§ã‚‚ã€å®Ÿã¯ã€Œãã‚‚ãã‚‚æ”¹å–„åŠªåŠ›ã¨æ•°å—ã¨ã¯ä½•ã®é–¢ä¿‚ã‚‚ãªã‹
gologo13 2014/11/16
marketing

management

statistics

kpi

business
ãƒªãƒ³ã‚¯
çµ±è¨ˆã®æ•™ç§‘æ›¸ã‚’å…¬é–‹ - ã€å°æ³¢ã®äº¬å¥³æ—¥è¨˜ã€‘(2013-03-12)
_ çµ±è¨ˆã®æ•™ç§‘æ›¸ã‚’å…¬é–‹ 2013å¹´åº¦ã®å¦éƒ¨ã®è¬›ç¾©ã€Œçµ±è¨ˆå¦ã€ã§ä½¿ç”¨ã™ã‚‹ãŸã‚ã®æ•™ç§‘æ›¸ã‚’å…¬é–‹ã—ã¾ã™ï¼Ž http://ruby.kyoto-wu.ac.jp/~konami/Text/ ã“ã®ãƒ†ã‚ã‚¹ãƒˆã¯ï¼Œä»Šå¹´åº¦ã¾ã§ã‚ã‚‹å‡ºç‰ˆç¤¾ã‹ã‚‰å‡ºã—ã¦ã‚‚ã‚‰ã£ã¦ã„ãŸã®ã§ã™ãŒï¼Œã‹ãªã‚Šã®è¨‚æ£ã¨åŠ ç†ã‚’è¡Œã„ï¼Œå…ƒã®æœ¬ã‹ã‚‰ã¯ã‹ãªã‚Šå†…å®¹ãŒé›¢ã‚Œã¦ãã¦ã—ã¾ã„ã¾ã—ãŸï¼Žã¾ãŸå‡ºç‰ˆç¤¾ã‚‚ï¼Œå£²ã‚Œè¡ŒããŒãœã‚“ãœã‚“æ‚ªã„ã—ï¼Œã‚«ãƒªã‚ãƒ¥ãƒ©ãƒ ãŒå¤‰ã‚ã£ã¦100äººä»¥ä¸Šã„ãŸå—è¬›è€…ãŒ30äººç¨‹åº¦ã«æ¿€æ¸›ã—ã¦å„²ã‘ã®ã‚¿ãƒã«ãªã‚‰ãªããªã‚Šï¼Œæ›¸åº—ã‹ã‚‰å¼•ãä¸Šã’ã¦ã—ã¾ã£ãŸã‚ˆã†ã§ã™ï¼Ž ãã“ã§ï¼Œæ€ã„åˆ‡ã£ã¦æ”¹è¨‚ç‰ˆã¯ãƒãƒƒãƒˆã«å…¬é–‹ã—ã¦ä¸€èˆ¬ã®äººã«è‡ªç”±ã«ä½¿ã£ã¦ã‚‚ã‚‰ã„ï¼Œå¦ç”ŸãŒæŽˆæ¥ã§ä½¿ã†åˆ†ã«ã¤ã„ã¦ã¯ï¼Œå°éƒ¨æ•°å°åˆ·ã®æ¥è€…ã«å¿…è¦ãªã¶ã‚“ã ã‘å°åˆ·è£½æœ¬ã—ã¦ã‚‚ã‚‰ã†ã“ã¨ã«ã—ã¾ã—ãŸï¼Žãªã‚“ã¨2æ—¥ã§è£½æœ¬ã¾ã§ã‚„ã£ã¦ãã‚Œã‚‹ã¨ã„ã†ã“ã¨ã§ï¼Œåˆå›žã®æŽˆæ¥ã§æ³¨æ–‡ã‚’ã¨ã£ã¦ã‹ã‚‰å°åˆ·ã™ã‚Œã°ï¼Œæ¬¡ã®è¬›ç¾©ã§ã¯ä½¿ãˆã‚‹ã‚ã‘ã§ã™ï¼Žä¾¿åˆ©ãªä¸–ã®ä¸ã§ã™ï¼Ž ã©ã‚“ãªæ•™
gologo13 2013/03/12
book

math

statistics
ãƒªãƒ³ã‚¯
H24:Introduction to Statistical Topic Models
çµ±è¨ˆæ•°ç†ç ”ç©¶æ‰€ H24å¹´åº¦å…¬é–‹è¬›åº§ ã€Œç¢ºçŽ‡çš„ãƒˆãƒ”ãƒƒã‚¯ãƒ¢ãƒ‡ãƒ«ã€ã‚µãƒãƒ¼ãƒˆãƒšãƒ¼ã‚¸ è¬›å¸«: æŒæ©‹å¤§åœ° (çµ±æ•°ç ”), çŸ³é»’å‹å½¦ (NTTã‚³ãƒŸãƒ¥ãƒ‹ã‚±ãƒ¼ã‚·ãƒ§ãƒ³ç§‘å¦åŸºç¤Žç ”ç©¶æ‰€) è¬›ç¾©ã‚¹ãƒ©ã‚¤ãƒ‰ æŒæ©‹åˆ† (2013/1/15) [PDF] (12MB) çŸ³é»’åˆ† (2013/1/16) [PDF] ã‚½ãƒ•ãƒˆã‚¦ã‚§ã‚¢ UM (Unigram Mixtures) um-0.1.tar.gz DM (Dirichlet Mixtures) dm-0.1.tar.gz, dm-0.2.tar.gz PLSI (Probabilistic Latent Semantic Indexing) plsi-0.03.tar.gz (å¤–éƒ¨ã‚µã‚¤ãƒˆ) LDA (Latent Dirichlet Allocation) lda-0.1.tar.gz å‚è€ƒæ–‡çŒ® ã€Œç§ã®ãƒ–ãƒƒã‚¯ãƒžãƒ¼ã‚¯: Latent Topic Model (æ½œåœ¨çš„ãƒˆãƒ”ãƒƒã‚¯ãƒ¢ãƒ‡
gologo13 2013/01/21
topicmodel

PDF

statistics

machinelearning
ãƒªãƒ³ã‚¯
ãƒ‡ãƒ¼ã‚¿ã‚µã‚¤ã‚¨ãƒ³ãƒ†ã‚£ã‚¹ãƒˆã§ã¯ãªã„äººã«çŸ¥ã£ã¦ãŠã„ã¦æ¬²ã—ã„äº‹ - hotokuã¨ã¯
çµ±è¨ˆã‚’å°‚é–€ã«ã—ã¦ã„ã‚‹è¨³ã§ã¯ãªã„äººã¨è©±ã—ã¦ã„ã¦æ„Ÿã˜ãŸé•å’Œæ„ŸãŒã‚ã£ãŸã®ã§ã€æ›¸ãç•™ã‚ã¦ãŠããŸã„ã€‚ ç–‘ã†ã¹ãé †ç•ªã¯ ãƒ¢ãƒ‡ãƒ« â†’ æŽ¨å®šæ³• ãƒ‡ãƒ¼ã‚¿åˆ†æžã‚’ã—ã¦ã„ã‚Œã°ã€å½“ç„¶ã€æœŸå¾…ã‚’æŽ›ã‘ãŸãƒ¢ãƒ‡ãƒ«ã®ãƒ‡ãƒ¼ã‚¿ã¸ã®å½“ã¦ã¯ã¾ã‚Šãã†ãŒæ‚ªã„ã¨ã„ã†äº‹ãŒåº¦ã€…ã‚ã‚‹ã€‚ã“ã†ã„ã†æ™‚ã€çµ±è¨ˆå±‹ã•ã‚“ã¨ã—ã¦è‡ªç„¶ã«æµ®ã‹ã¶ã®ã¯ã€Œãƒ¢ãƒ‡ãƒ«ãŒé–“é•ã£ã¦ã„ã‚‹ã€ã¨ã„ã†ç™ºæƒ³ã§ã‚ã‚‹ã€‚ã¨æ€ã†ã®ã ãŒã€éžçµ±è¨ˆå±‹ã•ã‚“ã¨è©±ã—ã¦ã„ã‚‹ã¨ã€ã“ã®ã‚ˆã†ãªæ™‚ã«ã€Œåˆ¥ã®æŽ¨å®šæ³•ã‚’è©¦ã—ã¦ã¿ãŸã‚‰ã©ã†ã ã‚ã†ã€ã¨è¨€ã‚ã‚Œã‚‹äº‹ãŒã‚ã‚‹ã€‚å¤šåˆ†ã€ç›®ã®å‰ã®ãƒ¢ãƒ‡ãƒ«ã«å¯¾ã™ã‚‹éŽåº¦ã®æœŸå¾…ã‹ã‚‰æ¥ã‚‹ã®ã ã‚ã†ã¨æ€ã†ãŒã€ã“ã®ã‚ˆã†ãªæ…‹åº¦ã§ã¯çµ±è¨ˆçš„ã«è¦‹ã‚‹ã¨å¦¥å½“æ€§ã‚’æ¬ ã„ãŸåˆ†æžã‚’ã—ã¦ã—ã¾ã†å±é™ºã‚’å•ã‚“ã§ã„ã‚‹ã€‚ ã²ã¨ã¤ã®äº‹ä¾‹ ã¨ã‚ã‚‹ç·šåž‹çŠ¶æ…‹ç©ºé–“ãƒ¢ãƒ‡ãƒ«ã®ãƒ‘ãƒ©ãƒ¡ãƒ¼ã‚¿ã‚’æŽ¨å®šã—ãŸæ‰€ã€ã©ã†ã—ã¦ã‚‚ãƒ‡ãƒ¼ã‚¿ã«åˆã‚ãªã„éƒ¨åˆ†ãŒã‚ã£ãŸã€‚å®Ÿã¯ã€ãã‚ŒãŒåˆã‚ãªã„ç†ç”±ã¯ç°¡å˜ã§ã€ã‚ã‚‹æ½œåœ¨å¤‰æ•°ã¯å¸¸ã«æ£ã§ã‚ã‚‹ã¯ãšãªã®ã ã€‚ç·šåž‹çŠ¶æ…‹ç©ºé–“ãƒ¢ãƒ‡ãƒ«ã§ã¯ã€æ½œåœ¨å¤‰æ•°ã®åˆ†å¸ƒã¯æ£è¦åˆ†å¸ƒã§
gologo13 2012/12/22
DataMining

statistics
ãƒªãƒ³ã‚¯
æ—¥æœ¬çµ±è¨ˆå¦ä¼šå‰µç«‹75å‘¨å¹´è¨˜å¿µå‡ºç‰ˆã€Ž21ä¸–ç´€ã®çµ±è¨ˆç§‘å¦ã€(å…¨3å·») å¢—è£œHPç‰ˆ (2012å¹´1æœˆ)
2008å¹´ã«æ—¥æœ¬çµ±è¨ˆå¦ä¼š75å‘¨å¹´è¨˜å¿µå‡ºç‰ˆã¨ã—ã¦åˆŠè¡Œã•ã‚Œã¾ã—ãŸå›½å‹ç›´äººãƒ»å±±æœ¬æ‹“ç›£ä¿®ã€Ž21ä¸–ç´€ã®çµ±è¨ˆç§‘å¦ã€(å…¨3å·»)ã®å¢—è£œç‰ˆã‚’ï¼Œ æ—¥æœ¬çµ±è¨ˆå¦ä¼šã®ãƒ›ãƒ¼ãƒ ãƒšãƒ¼ã‚¸ã‚’é€šã˜ã¦æä¾›ã™ã‚‹ã“ã¨ã¨ãªã‚Šã¾ã—ãŸï¼Žæœ¬æ›¸ã®å¢—è£œç‰ˆã® ã‚ªãƒ³ãƒ©ã‚¤ãƒ³å…¬é–‹ãŒçµ±è¨ˆç§‘å¦ã®ä»Šå¾Œã®ç™ºå±•ã«è³‡ã™ã‚‹ã“ã¨ã‚’æœŸå¾…ã—ã¦ãŠã‚Šã¾ã™ï¼Ž ç¬¬Iå·» ç¤¾ä¼šãƒ»çµŒæ¸ˆã®çµ±è¨ˆç§‘å¦ (å›½å‹ç›´äººãƒ»å±±æœ¬æ‹“ç·¨) ç¬¬IIå·» è‡ªç„¶ãƒ»ç”Ÿç‰©ãƒ»å¥åº·ã®çµ±è¨ˆç§‘å¦ (å°è¥¿è²žå‰‡ãƒ»å›½å‹ç›´äººç·¨) ç¬¬IIIå·» æ•°ç†ãƒ»è¨ˆç®—ã®çµ±è¨ˆç§‘å¦ (åŒ—å·æºå››éƒŽãƒ»ç«¹æ‘å½°é€šç·¨)
gologo13 2012/03/19
statistics

book
ãƒªãƒ³ã‚¯
ã¯ã¦ãªãƒ–ãƒã‚° | ç„¡æ–™ãƒ–ãƒã‚°ã‚’ä½œæˆã—ã‚ˆã†
ã¬ã‹ã„ã‚ã—ã€ã‚¿ãƒ¬ã‚«ãƒ„ã€é¶ã®åŠèº«æšã’ã€ãã—ã¦ç¬¹å›£åã€‚æ–°æ½Ÿã‚’å¹³ã‚‰ã’ã‚‹ï¼’æ³Šï¼“æ—¥ ä»Šå›žã®æ—…ã®ç›®çš„åœ°ã¯æ–°æ½Ÿã€‚æ–°æ½Ÿã«ã¯ç”³ã—è¨³ãªã„ã®ã ãŒã€è‡ªç™ºçš„ã«æ±ºã‚ãŸç›®çš„åœ°ã§ã¯ãªã„ã€‚ï¼ªï¼²æ±æ—¥æœ¬ã®ã€Œã©ã“ã‹ã«ãƒ“ãƒ¥ãƒ¼ãƒ¼ãƒ³ï¼ã€ã¨ã„ã†è¡Œãå…ˆã‚¬ãƒãƒ£ã‚µãƒ¼ãƒ“ã‚¹ã«ã‚ˆã‚Šæ±ºå®šã•ã‚ŒãŸã®ã ã€‚æ–°æ½Ÿã¨ã„ãˆã°é…’ã©ã“ã‚ç±³ã©ã“ã‚ã€‚ã¡ã‚‡ã†ã©æ–°ç±³ã®å£ç¯€ã ã€‚ã©ã‚“ã¨ã“ã„ã€‚å¤ç”ºã‚¨ãƒªã‚¢ã«å®¿ã‚’å–ã£â€¦
gologo13 2012/01/28
lecture

statistics
ãƒªãƒ³ã‚¯
Statistics 110: Introduction to Probability - Download free content from Harvard University on iTunes
Description Statistics 110 (Introduction to Probability), taught at Harvard University by Joe Bl itzstein in Fall 2011. Lecture videos, homework, review material, practice exams, and a large collection of practice probl ems with detailed solutions are provided. This course is an introduction to probability as a language and set of tools for understanding statistics, science, risk, and randomness. Th
gologo13 2012/01/19
statistics
ãƒªãƒ³ã‚¯
çµ±è¨ˆå¦ã‚’å‹‰å¼·ã™ã‚‹ã¨ãã«çŸ¥ã£ã¦ãŠããŸã„10ãƒã‚¤ãƒ³ãƒˆ - Isseiâ€™s Analysis ï½žãŠã¨ã†ã•ã‚“ã®è§£æžæ—¥è¨˜ï½ž
googleã•ã‚“ã‚„ãƒžã‚¤ã‚¯ãƒã‚½ãƒ•ãƒˆã•ã‚“ã¯ã€Œæ¬¡ã®10å¹´ã§ç†±ã„è·æ¥ã¯çµ±è¨ˆå¦ã€ã¨è¨€ã£ã¦ã„ã‚‹ã‚ˆã†ã§ã™ã€‚ã¾ãŸIBMã¯åˆ†æžãŒã§ãã‚‹äººæã‚’4,000äººå¢—ã‚„ã™ã¨è¨€ã£ã¦ã„ã¾ã™ï¼ˆåŒè¨˜äº‹ï¼‰ã€‚ã—ã‹ã—åˆ†æžã‚’ã™ã‚‹ã¨ãã®åŸºç¤Žçš„ãªå¦å•ã¯çµ±è¨ˆå¦ã§ã™ãŒã€ã„ã–çµ±è¨ˆå¦ã‚’å‹‰å¼·ã—ã‚ˆã†ã¨ã—ã¦ã‚‚ã©ã“ã‹ã‚‰å–ã‚Šã‹ã‹ã‚Œã°ã„ã„ã‹åˆ†ã‹ã‚‰ãªã‹ãã¦å›°ã‚‹ã¨ã„ã†è©±ã‚’ã‚ˆãèžãã¾ã™ã€‚ãã‚Œã«æ©Ÿæ¢°å¦ç¿’ç³»ã®æœ¬ã¯æœ€è¿‘å¢—ãˆã¦ãã¾ã—ãŸãŒã€çµ±è¨ˆå¦è‡ªä½“ãŒåŸºç¤Žã‹ã‚‰å¦ã¹ã‚‹æœ¬ã¯ã¾ã ã‚ã¾ã‚Šè¦‹ã‹ã‘ãªã„ã§ã™ã€‚ ãã“ã§ä»Šå›žã¯ã€çµ±è¨ˆå¦ã‚’åˆã‚ã¦å‹‰å¼·ã™ã‚‹ã¨ãã«çŸ¥ã£ã¦ãŠã„ãŸæ–¹ãŒè‰¯ã„10ãƒã‚¤ãƒ³ãƒˆã‚’ç´¹ä»‹ã—ãŸã„ã¨æ€ã„ã¾ã™ã€‚ 1. åŒã˜æ‰‹æ³•ãªã®ã«é•ã†å‘¼ã³åãŒä»˜ã„ã¦ã„ã‚‹ åˆ¥ã®äººãŒé•ã†åˆ†é‡Žã§ææ¡ˆã—ãŸæ‰‹æ³•ãŒã€å®Ÿã¯å…¨ãåŒã˜ã ã£ãŸã¨ã„ã†ã“ã¨ãŒã‚ã‚Šã¾ã™ã€‚è‰¯ãèžãã®ã¯ã€æ•°é‡åŒ–ç†è«–ã‚„åˆ†æ•£åˆ†æžã«ã¤ã„ã¦ã§ã™ã€‚ æ•°é‡åŒ–ç†è«– æ•°é‡åŒ–Ié¡ž = ãƒ€ãƒŸãƒ¼å¤‰æ•°ã«ã‚ˆã‚‹ç·šå½¢å›žå¸° æ•°é‡åŒ–IIé¡ž = ãƒ€ãƒŸãƒ¼å¤‰æ•°ã«ã‚ˆã‚‹åˆ¤åˆ¥åˆ†æž æ•°é‡åŒ–IIIé¡ž =
gologo13 2011/12/26
statistics
ãƒªãƒ³ã‚¯
Graphical Modelsã€€A Brief Introduction to Graphical Models and Bayesian Networks By Kevin Murphy, 1998.
A Brief Introduction to Graphical Models and Bayesian Networks By Kevin Murphy, 1998. "Graphical models are a marriage between probability theory and graph theory. They provide a natural tool for dealing with two probl ems that occur throughout applied mathematics and engineering -- uncertainty and complexity -- and in particular they are playing an increasingly important role in the design and ana
gologo13 2011/05/18
statistics
ãƒªãƒ³ã‚¯
Conjugate prior - Wikipedia
In Bayesian probability theory, if, given a likelihood function , the posterior distribution is in the same probability distribution family as the prior probability distribution , the prior and posterior are then called conjugate distributions with respect to that likelihood function and the prior is called a conjugate prior for the likelihood function . A conjugate prior is an algebraic convenien
gologo13 2011/05/18
ãƒ™ã‚¤ã‚ºæŽ¨è«–ã§ã€äº‹å¾Œåˆ†å¸ƒã¨å°¤åº¦é–¢æ•°Ã—äº‹å‰åˆ†å¸ƒãŒåŒã˜å½¢çŠ¶ã«ãªã£ã¦æ¬²ã—ã„ã€‚â†’äº‹å‰åˆ†å¸ƒã«å…±å½¹äº‹å‰åˆ†å¸ƒã‚’ç”¨ã„ã‚Œã°ã‚ˆã„ã€‚/ã‚¬ã‚¦ã‚¹åˆ†å¸ƒã¯è‡ªåˆ†è‡ªèº«ãŒå…±å½¹åˆ†å¸ƒ(self-conjugate)

statistics
ãƒªãƒ³ã‚¯
ãƒ™ã‚¤ã‚ºçµ±è¨ˆã¨çµ±è¨ˆç‰©ç†(çµ±è¨ˆç§‘å¦ã®ãƒ•ãƒãƒ³ãƒ†ã‚£ã‚¢12ã€€è¨ˆç®—çµ±è¨ˆIIã€€ãƒžãƒ«ã‚³ãƒ•é€£éŽ–ãƒ¢ãƒ³ãƒ†ã‚«ãƒ«ãƒæ³•ã®åŸºç¤Ž)
ã‚µãƒãƒ¼ãƒˆãƒšãƒ¼ã‚¸ (ãƒ™ã‚¤ã‚ºçµ±è¨ˆ ï¼† ãƒžãƒ«ã‚³ãƒ•é€£éŽ–ãƒ¢ãƒ³ãƒ†ã‚«ãƒ«ãƒ) 2008å¹´åº¦ã‚ˆã‚Šï¼Œæ–°ã‚·ãƒªãƒ¼ã‚ºã€Œç¢ºçŽ‡ã¨æƒ…å ±ã®ç§‘å¦ã€ãŒåˆŠè¡Œé–‹å§‹ã¨ãªã‚Šã¾ã—ãŸï¼Ž ä¼Šåºã¯ç·¨é›†ã®ã¿ã§åŸ·ç†ã®äºˆå®šã¯ã‚ã‚Šã¾ã›ã‚“ãŒï¼Œãã‚Œã¨ã¯åˆ¥ã«æ–°ä½œã®è¨ˆç”»ã‚‚ç·´ã£ã¦ãŠã‚Šã¾ã™ï¼Ž ã€Œçµ±è¨ˆç§‘å¦ã®ãƒ•ãƒãƒ³ãƒ†ã‚£ã‚¢ã€12å·»ã€Žè¨ˆç®—çµ±è¨ˆIIã€ã¯å¥½è©•6åˆ·ã‚Šã¨ãªã‚Šã¾ã—ãŸï¼Ž ä¼Šåºã¯ï¼Œç¬¬Iéƒ¨ã€Œãƒžãƒ«ã‚³ãƒ•é€£éŽ–ãƒ¢ãƒ³ãƒ†ã‚«ãƒ«ãƒæ³•ã®åŸºç¤Žã€ï¼Œè£œè«–Aã€Œé€æ¬¡ãƒ¢ãƒ³ãƒ†ã‚«ãƒ«ãƒæ³• å…¥é–€ã€ã‚’åŸ·ç†ã—ã¦ãŠã‚Šã¾ã™ï¼Ž ã¾ãŸï¼ŒãŠã‹ã’ã•ã¾ã§ã€Œãƒ™ã‚¤ã‚ºçµ±è¨ˆã¨çµ±è¨ˆç‰©ç†ã€ã‚‚5800éƒ¨ä»¥ä¸Š(7åˆ·ã‚Š)ã‚’è¶…ãˆã¾ã—ãŸï¼Žãƒ•ãƒãƒ³ãƒ†ã‚£ã‚¢ï¼Œç¢ºçŽ‡ã¨æƒ…å ±ã®ç§‘å¦ã¨ã‚‚ã©ã‚‚ã‚ˆã‚ã—ããŠé¡˜ã„ã—ã¾ã™ï¼Ž â€• ä¼Šåºå¹¸äºº åº—é ã«ãªã„æ›¸åº—ã‚‚å¤šã„ã®ã§ï¼Œã”å‹äººã‚„ã”åŒåƒšã®æ–¹ã«ãŠå‹§ã‚ã®å ´åˆï¼Œã”è³¼å…¥ã¯ã‚¢ãƒžã‚¾ãƒ³ã¾ãŸã¯å²©æ³¢æ›¸åº—ã®ç›´å£²ã‚’åˆ©ç”¨ã—ã¦ã„ãŸã ãã®ãŒç¢ºå®Ÿãªæ—¨ï¼ŒãŠä¼ãˆãã ã•ã‚‹ã¨å¹¸ã„ã§ã™ï¼Ž çµ±è¨ˆç§‘å¦ã®ãƒ•ãƒãƒ³ãƒ†ã‚£ã‚¢12ã€€è¨ˆç®—çµ±è¨ˆIIã€€ãƒžãƒ«ã‚³ãƒ•é€£éŽ–ãƒ¢ãƒ³ãƒ†ã‚«ãƒ«ãƒæ³•ã®åŸºç¤Ž ã‚¢ãƒžã‚¾
gologo13 2011/04/24
book

statistics
ãƒªãƒ³ã‚¯
å²©æ³¢æ›¸åº—
å²©æ³¢æ›¸åº—å…¬å¼ã‚µã‚¤ãƒˆ
gologo13 2011/04/24
statistics

DataMining

book
ãƒªãƒ³ã‚¯
æ‰‹ã§èªã‚€çµ±è¨ˆ - æ›¸è©• - STATISTICS HACKS(å’Œè¨³) : 404 Blog Not Found
2008å¹´01æœˆ26æ—¥02:30 ã‚«ãƒ†ã‚´ãƒªæ›¸è©•/ç”»è©•/å“è©•Math æ‰‹ã§èªã‚€çµ±è¨ˆ - æ›¸è©• - STATISTICS HACKS(å’Œè¨³) ã‚ªãƒ©ã‚¤ãƒªãƒ¼ãƒ»ã‚¸ãƒ£ãƒ‘ãƒ³ã®çŸ¢é‡Žæ§˜ã‚ˆã‚ŠçŒ®æœ¬å¾¡ç¤¼ã€‚ STATISTICS HACKS Bruce Frey é´¨æ¾¤çœžå¤«ç›£è¨³ / è¥¿æ²¢ç›´æœ¨è¨³ [åŽŸè‘—:STATISTICS HACKS] ã“ã‚Œã¯é¢ç™½ãã¦ä½¿ãˆã‚‹ã€‚ ãŸã ã—ã€ç«¶äº‰ã‚‚æ¿€ã—ã„åˆ†é‡Žã§ã‚‚ã‚ã‚‹ã€‚ æœ¬æ›¸ã€ã€ŒSTATISTICS HACKSã€ã¯ã€çµ±è¨ˆã‚’ãƒãƒƒã‚¯ã€ã™ãªã‚ã¡å®Ÿè·µã‚’é€šã—ã¦å¦ã¶æœ¬ã€‚ã€Œã‚¯ãƒ©ãƒƒã‚¯ã€ã®æ„å‘³ã§æµé€šã—ã¦ã„ã‚‹ã€Œãƒãƒƒã‚¯ã€ã‚’çµ±è¨ˆã«å¿œç”¨ã—ãŸã‚‚ã®ã«é–¢ã—ã¦ã¯ã€ã™ã§ã«ã€Œçµ±è¨ˆã§ã‚¦ã‚½ã‚’ã¤ãæ³•ã€ã¨ã„ã†ãƒãƒ³ã‚°ã‚»ãƒ©ãƒ¼ãŒã‚ã‚‹ã®ã§ãã¡ã‚‰ã‚’å‚ç…§ã€‚ ç›®æ¬¡ - oreilly.co.jp -- Online Catalog: Statistics Hacksã‚ˆã‚Š ã‚¯ãƒ¬ã‚¸ãƒƒãƒˆ ã¯ã˜ã‚ã« çµ±è¨ˆã®åŸºæœ¬ ç§˜å¯†ã‚’ç›—ã‚ 2ã¤ã®æ•°å—ã§ä¸–ç•Œã‚’è¨˜è¿° ã‚ªãƒƒã‚ºã®è¨ˆ
gologo13 2011/04/23
statistics
ãƒªãƒ³ã‚¯
404 Blog Not Found : æ›¸è©• - çµ±è¨ˆã‚’å¦ã¶ä¸‰å†Š+1
2010å¹´07æœˆ13æ—¥17:00 ã‚«ãƒ†ã‚´ãƒªæ›¸è©•/ç”»è©•/å“è©•Math æ›¸è©• - çµ±è¨ˆã‚’å¦ã¶ä¸‰å†Š+1 è¿”ç”ã«çª®ã™ã‚‹å•é¡Œã¨ã„ã†ã®ãŒã‚ã‚‹ã€‚ ç§ã®å ´åˆã€ãã®ã†ã¡ã®ä¸€ã¤ã¯ã€Œçµ±è¨ˆã‚’å¦ã¶ã¨ãã«èªã‚€ã¹ãæœ¬ã€ã¨ã„ã†ã‚‚ã®ã ã€‚ ã™ã”ãæ‚©ã¾ã—ã„ã€‚ç¾æ™‚ç‚¹ã§ã€Œç‰¹åŠ¹è–¬ã€ã¨ã‹ã€Œæ±ºå®šæ‰“ã€ã¨ã„ã†ã®ã¯è¦‹å½“ãŸã‚‰ãªã„ã€‚Perlã®ãƒ©ã‚¯ãƒ€æœ¬ã‚„ã€JavaScriptã®ã‚µã‚¤æœ¬ã¨ã„ã£ãŸã€ã„ã‚ã‚†ã‚‹ãƒã‚¤ãƒ–ãƒ«ã¯å˜åœ¨ã—ãªã„ã€‚ ãŒã€AIDSã«ã‚«ã‚¯ãƒ†ãƒ«ç™‚æ³•ãŒã‚ã‚‹ã‚ˆã†ã«ã€çµ±è¨ˆã‚‚ã‚«ã‚¯ãƒ†ãƒ«èªã¿ã§å¦ã¶äº‹ã¯å‡ºæ¥ã‚‹ã€‚ã¨ã„ã†ã‚ã‘ã§ç¾æ™‚ç‚¹ã§æœ€å¼·ã®ã‚«ã‚¯ãƒ†ãƒ«ãªã‚‰ã“ã†ãªã‚‹ã¨ã„ã†ã‚‚ã®ã‚’å–ã‚Šä¸Šã’ã¦ã¿ãŸã„ã€‚ çµè«–ã‹ã‚‰è¨€ã†ã¨ã€ã€Œãƒžãƒ³ã‚¬çµ±è¨ˆå¦å…¥é–€ã€ã§çµ±è¨ˆã®ä¸–ç•Œã‚’é³¥çž°ã—ã¤ã¤ã€ã€Œé‹ã¯æ•°å¦ã«ã¾ã‹ã›ãªã•ã„ã€ã§çµ±è¨ˆã®è½ã¨ã—ç©´ã«å®Ÿéš›ã«ã¯ã¾ã£ã¦ã¿ãªãŒã‚‰ã€ã€Œçµ±è¨ˆå¦å…¥é–€ (åŸºç¤Žçµ±è¨ˆå¦)ã€ã¨ã€ŒSTATISTICS HACKSã€ã®å•é¡Œã‚’æ‰‹ã‚’å‹•ã‹ã—ã¦è§£ã„ã¦ã¿ã‚‹ã€ã¨ã„ã†ã“ã¨ã«ãªã‚‹ã€‚ã†ã¡"STATISTICS H
gologo13 2011/04/23
statistics
ãƒªãƒ³ã‚¯
Elements of Statistical Learning: data mining, inference, and prediction. 2nd Edition.
gologo13 2011/04/16
book

statistics

MachineLearning
ãƒªãƒ³ã‚¯
http://case.f7.ems.okayama-u.ac.jp/statedu/hbw2-book/
gologo13 2010/06/11
statistics
ãƒªãƒ³ã‚¯
ãƒ”ã‚¢ã‚½ãƒ³ã®ç©çŽ‡ç›¸é–¢ä¿‚æ•°
è¨ˆç®—æ‰‹é †ï¼š 2 å¤‰æ•° $X$ï¼Œ$Y$ ãŒ $n$ çµ„ã‚ã‚‹ã¨ã™ã‚‹ã€‚ ãƒ”ã‚¢ã‚½ãƒ³ã®ç©çŽ‡ç›¸é–¢ä¿‚æ•° $r$ ã¯ï¼Œã€Œå¤‰æ•° $X$ã¨å¤‰æ•° $Y$ã®å…±åˆ†æ•£ã€ã¨ã€Œãã‚Œãžã‚Œã®å¤‰æ•°ã®æ¨™æº–åå·®ã€ã‹ã‚‰æ±‚ã‚ã‚‰ã‚Œã‚‹ã€‚ \[ \begin{align*} r &= \frac{\text{å¤‰æ•°}X\text{ã¨å¤‰æ•°}Y\text{ã®å…±åˆ†æ•£}}{\text{å¤‰æ•°}X\text{ã®æ¨™æº–åå·®} \times \text{å¤‰æ•°}Y\text{ã®æ¨™æº–åå·®}Y} \\ &= \frac{\displaystyle \frac{1}{n-1}\sum_{i=1}^n \left ( X_i-\bar{X} \right )\ \left ( Y_i-\bar{Y} \right )}{\sqrt{\displaystyle \frac{1}{n-1}\sum_{i=1}^n \left ( X_i-\bar{X} \right )^2
gologo13 2010/06/11
[

statistics
ãƒªãƒ³ã‚¯
çµ±è¨ˆå¦è‡ªç¿’ãƒŽãƒ¼ãƒˆ
å¤šå¤‰é‡è§£æž å›žå¸°åˆ†æžï¼ˆã‚ã¦ã¯ã‚ï¼‰ï¼Œåˆ¤åˆ¥åˆ†æžï¼Œä¸»æˆåˆ†åˆ†æžï¼Œå› ååˆ†æž, SEM æ•°é‡åŒ– I é¡žï¼Œæ•°é‡åŒ– II é¡žï¼Œæ•°é‡åŒ– III é¡žï¼Œæ•°é‡åŒ– IV é¡ž æ£æº–ç›¸é–¢åˆ†æžï¼Œã‚¯ãƒ©ã‚¹ã‚¿ãƒ¼åˆ†æžï¼Œä¸»åº§æ¨™åˆ†æž ã‚¯ãƒãƒ³ãƒãƒƒã‚¯ã® $\alpha$ ä¿¡é ¼æ€§ä¿‚æ•° ç”Ÿå˜çŽ‡è§£æž Cutler-Ederer æ³•ã«ã‚ˆã‚‹ç”Ÿå‘½è¡¨ï¼ŒKaplan-Meier æ³•ã«ã‚ˆã‚‹ç”Ÿå‘½è¡¨ å¤šé‡ãƒã‚¸ã‚¹ãƒ†ã‚£ãƒƒã‚¯ãƒ¢ãƒ‡ãƒ«ï¼ŒCox ã®æ¯”ä¾‹ãƒã‚¶ãƒ¼ãƒ‰ãƒ¢ãƒ‡ãƒ«
gologo13 2010/05/30
statistics

ã¾ã¨ã‚
ãƒªãƒ³ã‚¯
çµ±è¨ˆã‚½ãƒ•ãƒˆãƒ»çµ±è¨ˆå¦ç¿’ç”¨ãƒ‡ãƒ¼ã‚¿
çµ±è¨ˆé–¢ä¿‚ç·åˆãƒªãƒ³ã‚¯ï½œ çµ±è¨ˆã‚½ãƒ•ãƒˆç´¹ä»‹é–¢ä¿‚ï½œ çµ±è¨ˆã‚½ãƒ•ãƒˆä¼šç¤¾ï½œ å…±åˆ†æ•£æ§‹é€ ãƒ¢ãƒ‡ãƒ«ï½œ çµ±è¨ˆã‚½ãƒ•ãƒˆã€€ãŠã‚‚ã‚‰ã„å›(ãƒªãƒ³ã‚¯é›† |å…¨èˆ¬|å¤šå¤‰é‡è§£æžãƒ»å¤šæ¬¡å…ƒå°ºåº¦è§£æž|å¤šå¤‰é‡è§£æž |å¤šæ¬¡å…ƒå°ºåº¦è§£æž|åŸºç¤Žçµ±è¨ˆç‰| åˆ†å¸ƒ|ç‰¹å®šã‚¢ãƒ—ãƒªãƒžã‚¯ãƒ)ï½œ çµ±è¨ˆç”¨ãƒ‡ãƒ¼ã‚¿ï½œ WWWä¸Šã§ã®çµ±è¨ˆå‡¦ç†ï½œ äººï½œ æ—¥æœ¬ã®çµ±è¨ˆå¦é–¢ä¿‚ï½œ æ—¥æœ¬ã®ãƒ¡ãƒ¼ãƒªãƒ³ã‚°ãƒªã‚¹ãƒˆï½œ ï¼œï¼·ï¼·ï¼·ã§çµ±è¨ˆã‚’å¦ç¿’ã—ã‚ˆã†ï¼žï½œ æ¤œç´¢ç³»ç›¸è«‡ç‰ï½œ çµ±è¨ˆæ•™è‚²ãƒªãƒ³ã‚¯ï½œ çµ±è¨ˆå¦ç”¨èªžé›†ï½œ case study(å•é¡Œé›†)ï½œ çµ±è¨ˆå¦ã‹ã‚‰åˆ†æ•£åˆ†æžãƒ»é‡å›žå¸°ã¾ã§ï½œ ç·åˆçš„ï½œ ç‰¹å®šåˆ†é‡Ž ï¼ˆ çµ±è¨ˆæ•™è‚²ãƒ»æ³¨æ„ï½œ ç ”ç©¶æ³•ï½œ æ´å²ï½œ æ¸¬å®šï½œ ã‚µãƒ³ãƒ—ãƒªãƒ³ã‚°ï½œ åˆ†å¸ƒï½œ æ¤œå®šåŠ›ï½œ meta-analysisï½œ resamplingï½œ exact testï½œ conjoint analysisï½œ å®Ÿé¨“è¨ˆç”»æ³•ãƒ»åˆ†æ•£åˆ†æž ï½œ å¤šå¤‰é‡è§£æžï½œ å› ååˆ†æžãƒ»å…±åˆ†æ•£æ§‹é€ ãƒ¢ãƒ‡ãƒ«ï½œ multi levelï½œ å¤šæ¬¡å…ƒå°ºåº¦è§£æžï½œ ã‚°
gologo13 2010/05/28
statistics

links
ãƒªãƒ³ã‚¯
Rè¨€èªžã«ã‚ˆã‚‹çµ±è¨ˆå¦
Rè¨€èªžã«ã‚ˆã‚‹çµ±è¨ˆå¦ * ã¯ã˜ã‚ã« * åŸºæœ¬çš„çµ±è¨ˆé‡ ** åˆ†æ•£ *** å…±åˆ†æ•£ *** å…±åˆ†æ•£è¡Œåˆ— *** ç›¸é–¢ä¿‚æ•° *** åˆ†æ•£å…±åˆ†æ•£è¡Œåˆ— ** æ¨™æº–åå·® ** æ¨™æº–èª¤å·® * åˆ†æ•£åˆ†æžã®åŽŸç† ** ä¸€å…ƒé…ç½®åˆ†æ•£åˆ†æž ** äºŒå…ƒé…ç½®åˆ†æ•£åˆ†æž * Ï‡2ä¹—æ¤œå®š * é‡å›žå¸°åˆ†æž ** æ¨™æº–åå›žå¸°ä¿‚æ•° ** 3æ¬¡å…ƒæ•£å¸ƒå›³ * å› ååˆ†æž ** éª¨å¯†åº¦ãƒ‡ãƒ¼ã‚¿ã®ä¾‹ * å…±åˆ†æ•£æ§‹é€ åˆ†æž ** ä¸‹éƒ¨æ¶ˆåŒ–å™¨ç™Œã®ä¾‹1 ** ä¸‹éƒ¨æ¶ˆåŒ–å™¨ç™Œã®ä¾‹2 ** Kleinã®ä¾‹ ** ãƒ‘ã‚¹å›³ * å¤šé‡ãƒã‚¸ã‚¹ãƒ†ã‚£ãƒƒã‚¯è§£æž * ã‚¯ãƒãƒ¼ãƒ³ãƒãƒƒãƒÎ±ä¿‚æ•° * å…¥å‡ºåŠ› ** ãƒ•ã‚¡ã‚¤ãƒ«å‡ºåŠ› * ã¯ã˜ã‚ã« > ã§å§‹ã¾ã‚‹è¡Œã¯Rã¸ã®ã‚³ãƒžãƒ³ãƒ‰å…¥åŠ›ã‚’è¡¨ã—ã¾ã™ã€‚ä¸€æ–¹ã€%ã§å§‹ã¾ã‚‹è¡Œã¯ã€ã‚¿ãƒ¼ãƒŸãƒŠãƒ«ã‹ã‚‰ã®å…¥åŠ›ã‚’æ„å‘³ã—ã¾ã™ã€‚Rã‹ã‚‰ã® å‡ºåŠ›ã¯ç‰¹ã«åŒºåˆ¥ã—ã¦ã„ã¾ã›ã‚“ãŒã€æ–‡è„ˆã‹ã‚‰æ˜Žã‚‰ã‹ã§ã—ã‚‡ã†ã€‚ ãªãŠã€ç§ã®å®Ÿè¡Œç’°å¢ƒã¯ã€ Debian GNU/Linux woody
gologo13 2010/05/27
R_Language

statistics
ãƒªãƒ³ã‚¯
1 2 æ¬¡ã®ãƒšãƒ¼ã‚¸