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Main result
1.
P„” - LD|
U`µÄt` äµ RD t© ôtY µÄ„ : èÀÉ, äÀÉ„ 2ü( : Table2 main result @Ä- ¸YP ôtYÐ )XYPä „X, ´íYPä ¬ü March 11, 2014 @Ä- RD t© ôtY µÄ„ : èÀÉ, äÀÉ„
2.
P„” - LD|
U`µÄt` äµ ©( 1 P„” 2 - LD| U`µÄt` Frequentist VS Bayesian Likelihood ŒÀ„X PÀ ”• „°üÐ ì¨ ü X 3 äµ @Ä- RD t© ôtY µÄ„ : èÀÉ, äÀÉ„
3.
P„” - LD|
U`µÄt` äµ ÝX èT Y ð? 1 ÄÜ ð VS Üð(Count data) 2 ð: Ü„ì!!!!!! ! | ŒÀ„ 3 Count: Ý , @ etc.. : ìD¡, È, Ltm ñ..(ݵ) Y ”ü? 1 2”ü VS 3”ütÁ 2 2”ü : À¤ñ 3 3”ütÁ : W ñ..(ݵ) @Ä- RD t© ôtY µÄ„ : èÀÉ, äÀÉ„
4.
P„” - LD|
U`µÄt` äµ èÀÉ VS äÀÉ èÀÉ(univariate) VS äÀÉ(multivariate) 1 Association ¼È˜ ˆÐ 1 äx ƒX ¨ü| ô ÄÐÄ Associationt ˆ”? @Ä- RD t© ôtY µÄ„ : èÀÉ, äÀÉ„
5.
P„” - LD|
U`µÄt` äµ Frequentist VS Bayesian Likelihood ŒÀ„X PÀ ”• „°üÐ ì¨ ü X VS ü U` ü¬| X8 1t ˜, U` 1 : U`@ U + t¬Xà øƒD ”ä. 2 ü: L Æä, ÿLD Ä Åpt¸` Ð.. ü¬| X8 1t ˜, U`Ð ü• 1 : Ä X8 ”tôÈ U`@ 1/6x ï Xä. 2 ü: 1/6| ƒ @p, Ä X8ôÈ 1/6t Þ” ƒ $.. @Ä- RD t© ôtY µÄ„ : èÀÉ, äÀÉ„
6.
P„” - LD|
U`µÄt` äµ Frequentist VS Bayesian Likelihood ŒÀ„X PÀ ”• „°üÐ ì¨ ü X Homo bayesianis Figure : Fun example of bayesian @Ä- RD t© ôtY µÄ„ : èÀÉ, äÀÉ„
7.
P„” - LD|
U`µÄt` äµ Frequentist VS Bayesian Likelihood ŒÀ„X PÀ ”• „°üÐ ì¨ ü X FrequentistX |Á• Á): à}t‘ 0t }t‘ UX¨ü (t Æ” ƒ @p.. ˜: P? à}t‘ 0t }t‘ (t 0t|à?? (t 0 t|à X. øìt ´LlLl.. t pt0X Áit ˜, ¥1t pX Æ”p(5%øÌxp)? øÈL
8.
À8´. 1 (t
0t|à Ð ¬Œ@ Æä. ÁXD œÀ. 2 Á)X ü¥D Œ tXì . 3 ½Xä. @Ä- RD t© ôtY µÄ„ : èÀÉ, äÀÉ„
9.
P„” - LD|
U`µÄt` äµ Frequentist VS Bayesian Likelihood ŒÀ„X PÀ ”• „°üÐ ì¨ ü X BayesianX |Á• Á): à}t‘ 0t }t‘ UX¨ü (t Æ” ƒ @p.. N(0; 1)„ì| 0tÀ JDL? ˜: (t N(0; 1)D 0xäà X. Ð 0tt t pt0X Áit ü´LD L, (tX pt€U`D Ä°tôÈ N(5; 1:2)| 0t”p? 1 ¬ÿLÐ „ì| : Prior 2 pt0 ü” ô: Likelihood 3 ÿLü pt0X ô| …i : Posterior- tx t. @Ä- RD t© ôtY µÄ„ : èÀÉ, äÀÉ„
10.
P„” - LD|
U`µÄt` äµ Frequentist VS Bayesian Likelihood ŒÀ„X PÀ ”• „°üÐ ì¨ ü X Probability‘ (t. ¥Ä Figure : Likelihood @Ä- RD t© ôtY µÄ„ : èÀÉ, äÀÉ„
11.
P„” - LD|
U`µÄt` äµ Frequentist VS Bayesian Likelihood ŒÀ„X PÀ ”• „°üÐ ì¨ ü X Maximum likelihood estimator(MLE) ¥Ä”É: 1; ; nt Žt|X. 1 X ¥Ä h| lä. 2 ¥Ä| € ñXt ´ ¬tX ¥Ä (ŽtÈL) 3 ¥Ä| X”
12.
| lä. @Ä-
RD t© ôtY µÄ„ : èÀÉ, äÀÉ„
13.
P„” - LD|
U`µÄt` äµ Frequentist VS Bayesian Likelihood ŒÀ„X PÀ ”• „°üÐ ì¨ ü X @ µÄ„ä 1ÄÐ 0x U(t 1 T-test@ ANOVA, simple regression@ @ µÄ„tä. Uü ˜t@X Ä 1 correlationü simple regression@ @ „. @Ä- RD t© ôtY µÄ„ : èÀÉ, äÀÉ„
14.
P„” - LD|
U`µÄt` äµ Frequentist VS Bayesian Likelihood ŒÀ„X PÀ ”• „°üÐ ì¨ ü X Least Square(Œñ•) ñiD Œ: y Ü1Ð D”Æä. Figure : Least square method @Ä- RD t© ôtY µÄ„ : èÀÉ, äÀÉ„
15.
P„” - LD|
U`µÄt` äµ Frequentist VS Bayesian Likelihood ŒÀ„X PÀ ”• „°üÐ ì¨ ü X MLE: ¥Ä”É pt0 |´ ¥1D : y” „ìD”. @Ä- RD t© ôtY µÄ„ : èÀÉ, äÀÉ„
16.
P„” - LD|
U`µÄt` äµ Frequentist VS Bayesian Likelihood ŒÀ„X PÀ ”• „°üÐ ì¨ ü X Why know? 1 Multilevel „X tt| t. 2 OLS ! GLS ! GEE : semi-parametric 3 MLE ! LMM ! GLMM : parametric @Ä- RD t© ôtY µÄ„ : èÀÉ, äÀÉ„
17.
P„” - LD|
U`µÄt` äµ Frequentist VS Bayesian Likelihood ŒÀ„X PÀ ”• „°üÐ ì¨ ü X LRT? Ward? score? Likelihood Ratio Test VS Ward test VS score test 1 µÄ X1 èX” )•ä. 2 ¥ÄDP VS ÀDP VS 0¸0DP/ @Ä- RD t© ôtY µÄ„ : èÀÉ, äÀÉ„
18.
P„” - LD|
U`µÄt` äµ Frequentist VS Bayesian Likelihood ŒÀ„X PÀ ”• „°üÐ ì¨ ü X DP Figure : Comparion @Ä- RD t© ôtY µÄ„ : èÀÉ, äÀÉ„
19.
P„” - LD|
U`µÄt` äµ Frequentist VS Bayesian Likelihood ŒÀ„X PÀ ”• „°üÐ ì¨ ü X AIC °¬ l ¨X ¥Ä| Lt| Xt. 1 AIC = 2 log (L) + 2 k 2 k: $…ÀX /(1Ä, ˜t, ð ...) 3 ‘D] ‹@ ¨!!! ¥Ä p ¨D àt ÀÌ.. $…À 4 Ît ˜ð!!! @Ä- RD t© ôtY µÄ„ : èÀÉ, äÀÉ„
20.
P„” - LD|
U`µÄt` äµ 1 Main tableÐ èÀÉ„°ü t ü ˆÄ].. 2 epicalc (¤À tƒD ¥XŒ tä. 3 Week2.R 11. @Ä- RD t© ôtY µÄ„ : èÀÉ, äÀÉ„
21.
P„” - LD|
U`µÄt` äµ END Email : secondmath85@gmail.com Oce: (02)880-2473 H.P: 010-9192-5385 @Ä- RD t© ôtY µÄ„ : èÀÉ, äÀÉ„
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