Bayesian analysis of principal component regression model <sup>†</sup>
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전체 글
(2) 248. Minjung Kyung. 1957; Hotelling, 1957; Jeffers, 1967). ᄌ ᅮᄉ ᆼᄇ ᅥ ᆫᄒ ᅮ ᅬᄀ ᅱᄆ ᅩᄒ ᆼᄋ ᅧ ᆫ pᄀ ᅳ ᅢᄋ ᅴᄉ ᆯᅧ ᅥ ᆼ 며 ᆫ ᄇᄉ ᅮᄃ ᆯᄋ ᅳ ᅴᄉ ᆫᅧ ᅥ ᆼ ᄒᄌ ᅩᄒ ᆸᄋ ᅡ ᆫ pᄀ ᅵ ᅢᄋ ᅴᄌ ᅮᄉ ᆼ ᅥ ᆫ(ᄋ ᅮ ᄇ ᆫᄌ ᅪ ᆫᄀ ᅥ ᅨᄉ ᅮᄋ ᆫᅧ ᅵ ᆼ ᄀᄋ ᅮ)ᄋ ᆯᄉ ᅳ ᅡᄋ ᆼᄒ ᅭ ᅡᄂ ᆫᄀ ᅳ ᆺᄋ ᅥ ᅵᄋ ᅡᄂ ᅵᄅ ᅡ, p ᄀ ᅢᄋ ᅴᄀ ᆫᄎ ᅪ ᆨᄀ ᅳ ᅡᄂ ᆼᄒ ᅳ ᆫᄋ ᅡ ᆼᄌ ᅣ ᆨᅧ ᅥ ᆫ ᄇᄉ ᅮᄃ ᆯᄉ ᅳ ᅡᄋ ᅵᄋ ᅴᄇ ᆫᄉ ᅮ ᆫ-ᄀ ᅡ ᆼᅮ ᅩ ᆫ ᄇᄉ ᆫᄀ ᅡ ᆫᄀ ᅪ ᅨ ᆯᄉ ᅳ ᄅ ᆯᅧ ᅥ ᆼ ᄆᄒ ᆯᄉ ᅡ ᅮᄋ ᆻᄂ ᅵ ᆫᄇ ᅳ ᆫᄉ ᅮ ᆫᄋ ᅡ ᅵᄏ ᆫ K(<< p) ᄀ ᅳ ᅢᄋ ᅴᄌ ᅮᄉ ᆼᄇ ᅥ ᆫᄋ ᅮ ᆯᄉ ᅳ ᅡᄋ ᆼᄒ ᅭ ᆫᄃ ᅡ ᅡ. ᄋ ᅵᄅ ᅥᄒ ᆫᄌ ᅡ ᅮᄉ ᆼᄇ ᅥ ᆫᄋ ᅮ ᆫᄇ ᅳ ᆫᄉ ᅧ ᅮᄃ ᆯᄋ ᅳ ᆯᄎ ᅳ ᅬᄃ ᅢᄒ ᆫᄋ ᅡ ᅳᄅ ᅩ ᆯᄆ ᅥ ᄉ ᆼᄒ ᅧ ᆯᄉ ᅡ ᅮᄋ ᆻᄋ ᅵ ᅳᄆ ᆫᄉ ᅧ ᅥᄌ ᆨᄀ ᅵ ᅭᄉ ᆼᄋ ᅥ ᆯᄋ ᅳ ᅲᄌ ᅵᄒ ᅡᄀ ᅦᄀ ᅮᄒ ᅡᄆ ᅧᄄ ᅩᄒ ᆫᄀ ᅡ ᆨᄌ ᅡ ᅮᄉ ᆼᄇ ᅥ ᆫᄋ ᅮ ᆫᄌ ᅳ ᆫᄎ ᅥ ᅦᄀ ᆫᄎ ᅪ ᆨᄎ ᅳ ᅵᄋ ᅴᅡ ᆷ ᄒᄉ ᅮᄒ ᆼᄐ ᅧ ᅢᄅ ᅩᄌ ᆫᄎ ᅥ ᅦᄇ ᆫᄉ ᅧ ᅮ ᅦᄋ ᄋ ᅴᄌ ᆫᄌ ᅩ ᆨᄋ ᅥ ᆫᅧ ᅵ ᆫ ᄇᄉ ᅮᄅ ᅩᄒ ᅢᄉ ᆨᄒ ᅥ ᆯᄉ ᅡ ᅮᄋ ᆻᄃ ᅵ ᅡ. ᅮᄉ ᄌ ᆼᄇ ᅥ ᆫᄒ ᅮ ᅬᄀ ᅱᄆ ᅩᄒ ᆼᄋ ᅧ ᅴᄌ ᆼᄌ ᅡ ᆷᄌ ᅥ ᆼᄒ ᅮ ᅡᄂ ᅡᄂ ᆫᄉ ᅳ ᆫᄒ ᅥ ᆼᄆ ᅧ ᅩᄒ ᆼᄋ ᅧ ᅦᄉ ᅥᄆ ᅩᄉ ᅮᄋ ᅴᄎ ᅮᄅ ᆫᄆ ᅩ ᆾᄆ ᅵ ᅩᄒ ᆼᄋ ᅧ ᅴᄌ ᆨᄒ ᅥ ᆸᅥ ᅡ ᆼ ᄉᄋ ᅦᄆ ᆫᄌ ᅮ ᅦᄀ ᅡᄃ ᅬᄂ ᆫᄉ ᅳ ᆯᅧ ᅥ ᆼ ᄆᄇ ᆫ ᅧ ᅮᄋ ᄉ ᅴᄀ ᆺᄉ ᅢ ᅮᄀ ᅡᄀ ᆫᄎ ᅪ ᆨᄀ ᅳ ᆺᄉ ᅢ ᅮᄇ ᅩᄃ ᅡᄆ ᆭᄋ ᅡ ᆫᄌ ᅳ ᅡᄅ ᅭ (p > n)ᄋ ᅪᄃ ᅮᄀ ᅢᄋ ᅵᄉ ᆼᄋ ᅡ ᅴᄉ ᆯᄆ ᅥ ᆼᅧ ᅧ ᆫ ᄇᄉ ᅮᄃ ᆯᄉ ᅳ ᅡᄋ ᅵᄋ ᅦᄌ ᆫᄌ ᅩ ᅢᄒ ᅡᄂ ᆫᄃ ᅳ ᅡᄌ ᆼᄀ ᅮ ᆼᄉ ᅩ ᆫᅥ ᅥ ᆼ ᄉ (multicollinearity)ᄆ ᆫᄌ ᅮ ᅦᄅ ᆯᄉ ᅳ ᅥᄅ ᅩᄌ ᆨᄀ ᅵ ᅭᄀ ᅡᄃ ᅬᄂ ᆫᄌ ᅳ ᅮᄉ ᆼᄇ ᅥ ᆫᅳ ᅮ ᆯ ᄋᄒ ᅬᄀ ᅱᄇ ᆫᄉ ᅧ ᅮᄅ ᅩᄉ ᅡᄋ ᆼᄒ ᅭ ᅡᄋ ᅧᄒ ᅢᄀ ᆯᄒ ᅧ ᆫᄃ ᅡ ᅡ. ᄇ ᆫᄉ ᅮ ᆫᄋ ᅡ ᅵᄌ ᆨᄋ ᅡ ᆫᄌ ᅳ ᅮ ᆼᄇ ᅥ ᄉ ᆫᅳ ᅮ ᆯ ᄋᄆ ᅩᄒ ᆼᄋ ᅧ ᅦᄉ ᅡᄋ ᆼᄒ ᅭ ᅡᄌ ᅵᄋ ᆭᄀ ᅡ ᅩ, ᄇ ᆫᄉ ᅮ ᆫᄋ ᅡ ᅵᆫ ᄏ ᅳᄇ ᅮᄇ ᆫᄌ ᅮ ᆸᄒ ᅵ ᆸ (subset) ᄌ ᅡ ᅮᄉ ᆼᄇ ᅥ ᆫᅳ ᅮ ᆯ ᄋᄒ ᅬᄀ ᅱᄇ ᆫᄉ ᅧ ᅮᄅ ᅩᄉ ᅡᄋ ᆼᄒ ᅭ ᅡᄋ ᅧᄎ ᅮᄌ ᆼᅧ ᅥ ᆫ ᄇᄉ ᅮᄋ ᅴ ᅮᄅ ᄉ ᆯᄌ ᅳ ᆯᄋ ᅮ ᅵᄂ ᆫᄇ ᅳ ᆫᄉ ᅧ ᅮᄎ ᆨᄉ ᅮ ᅩ (variable reduction)ᄋ ᅴᄇ ᆼᄇ ᅡ ᆸᄋ ᅥ ᅳᄅ ᅩᄉ ᅡᄋ ᆼᄃ ᅭ ᆫᄃ ᅬ ᅡ. ᄀ ᅳᄅ ᅵᄀ ᅩᄒ ᅬᄀ ᅱᄆ ᅩᄒ ᆼᄋ ᅧ ᅦᄉ ᅡᄋ ᆼᄃ ᅭ ᅬᄂ ᆫᄌ ᅳ ᆨᅥ ᅥ ᆯ ᄌ ᆫᄌ ᅡ ᄒ ᅮᄉ ᆼᄇ ᅥ ᆫᅳ ᅮ ᆯ ᄋᄉ ᆫᅢ ᅥ ᆨ ᄐᄒ ᆷᄋ ᅡ ᅳᄅ ᅩᄎ ᅮᄌ ᆼᄃ ᅥ ᆫᄆ ᅬ ᅩᄒ ᆼᄋ ᅧ ᆯᄇ ᅳ ᅡᄐ ᆼᄋ ᅡ ᅳᄅ ᅩᄇ ᆫᄋ ᅡ ᆼᄀ ᅳ ᆹᄋ ᅡ ᅦᄃ ᅢᄒ ᆫᄒ ᅡ ᅭᅪ ᄀᄌ ᆨᄋ ᅥ ᆫᄋ ᅵ ᅨᄎ ᆨᄋ ᅳ ᅵᄀ ᅡᄂ ᆼᄒ ᅳ ᅡᄃ ᅡ. ᄀ ᅳᄅ ᅥᄂ ᅡᄇ ᆫ ᅡ ᆼᄇ ᅳ ᄋ ᆫᅮ ᅧ ᄉᄋ ᅴᄋ ᅨᄎ ᆨᅳ ᅳ ᆯ ᄋᄆ ᆨᄌ ᅩ ᆨᄋ ᅥ ᅳᄅ ᅩᄒ ᅡᄂ ᆫᅮ ᅳ ᆫ ᄇᄉ ᆨᄋ ᅥ ᅦᄉ ᅥᄂ ᆫᄆ ᅳ ᅩᄉ ᅮᄎ ᅮᄌ ᆼᄋ ᅥ ᅦᄉ ᅡᄋ ᆼᄒ ᅭ ᅡᄌ ᅵᄋ ᆭᄂ ᅡ ᆫᅮ ᅳ ᆫ ᄇᄉ ᆫᄋ ᅡ ᅵᄌ ᆨᄋ ᅡ ᆫᄌ ᅳ ᅮᄉ ᆼᄇ ᅥ ᆫᄋ ᅮ ᆨᄉ ᅧ ᅵᄋ ᅨᄎ ᆨ ᅳ ᅦᄉ ᄋ ᅥᄂ ᆫᄌ ᅳ ᆼᄋ ᅮ ᅭᄒ ᆫᄋ ᅡ ᆨᄒ ᅧ ᆯᄋ ᅡ ᆯᄒ ᅳ ᆯᄉ ᅡ ᅮᄋ ᆻᄃ ᅵ ᅡᄂ ᆫᅮ ᅳ ᆫ ᄆᄌ ᅦᄀ ᅡᄌ ᅦᄀ ᅵᄃ ᆫᄃ ᅬ ᅡ. ᄇ ᅮᄇ ᆫᄌ ᅮ ᆸᄒ ᅵ ᆸᄋ ᅡ ᆯᄉ ᅳ ᆫᅢ ᅥ ᆨ ᄐᄒ ᅡᄂ ᆫᄀ ᅳ ᅪᄌ ᆼᄋ ᅥ ᅦᄉ ᅥᄇ ᆫᄋ ᅡ ᆼᄇ ᅳ ᆫᄉ ᅧ ᅮᄋ ᅪᄋ ᅴᄀ ᆫ ᅪ ᅨᄀ ᄀ ᅡᄋ ᅡᄂ ᆫᄃ ᅵ ᆫᄉ ᅡ ᆫᄒ ᅮ ᅵᄉ ᆯᅧ ᅥ ᆼ ᄆᄇ ᆫᄉ ᅧ ᅮᄃ ᆯᄋ ᅳ ᅴᄌ ᅮᄉ ᆼᄇ ᅥ ᆫᄃ ᅮ ᆯᅮ ᅳ ᆼ 주 ᆫ ᄇᄉ ᆫᄋ ᅡ ᅵᄏ ᆫᄉ ᅳ ᆼᄇ ᅥ ᆫᄆ ᅮ ᆫᄋ ᅡ ᆯᄀ ᅳ ᅩᄅ ᅧᄒ ᆫᄃ ᅡ ᅡᄂ ᆫᄌ ᅳ ᆷᄋ ᅥ ᅦᄉ ᅥ, ᄇ ᆫᄋ ᅡ ᆼᄇ ᅳ ᆫᄉ ᅧ ᅮᄋ ᅪᄋ ᅴᄀ ᆫᄀ ᅪ ᅨ ᆯᄀ ᅳ ᄅ ᅩᅧ ᄅᄒ ᅡᄌ ᅵᄋ ᆭᄋ ᅡ ᆫᄌ ᅳ ᅮᄉ ᆼᄇ ᅥ ᆫᄋ ᅮ ᅴᄉ ᅡᄋ ᆼᄋ ᅭ ᅵᄌ ᅮᄉ ᆼᄇ ᅥ ᆫᄒ ᅮ ᅬᄀ ᅱᄆ ᅩᄒ ᆼᄋ ᅧ ᅴᄋ ᅨᄎ ᆨᄅ ᅳ ᆨᄋ ᅧ ᆯᄄ ᅳ ᆯᄋ ᅥ ᅥᄄ ᅳᄅ ᆯᄉ ᅵ ᅮᄋ ᆻᄀ ᅵ ᅵᄄ ᅢᄆ ᆫᄋ ᅮ ᅵᄃ ᅡ. ᄋ ᅵᄅ ᆫᄆ ᅥ ᆫᄌ ᅮ ᅦ ᆯᄀ ᅳ ᄅ ᅩᅧ ᄅᄒ ᅡᄋ ᅧ, Bair ᄃ ᆼ (2006)ᄋ ᅳ ᆫᄌ ᅳ ᅵᄃ ᅩᄌ ᅮᄉ ᆼᄇ ᅥ ᆫ (supervised principal component) ᄒ ᅮ ᅬᄀ ᅱᄆ ᅩᄒ ᆼᄋ ᅧ ᆯᄌ ᅳ ᅦᄋ ᆫᄒ ᅡ ᅡᄋ ᆻᄃ ᅧ ᅡ. ᅵᄃ ᄌ ᅩᅮ ᄌᄉ ᆼᄇ ᅥ ᆫᄒ ᅮ ᅬᄀ ᅱᄆ ᅩᄒ ᆼᄋ ᅧ ᆫᄇ ᅳ ᆫᄋ ᅡ ᆼᄇ ᅳ ᆫᄉ ᅧ ᅮᅪ ᄋᄋ ᅴᄉ ᆼᄀ ᅡ ᆫᄉ ᅪ ᆼᄋ ᅥ ᅵᄇ ᆫᄀ ᅮ ᅨᄌ ᆷ (thresholder) ᄇ ᅥ ᅩᄃ ᅡᄂ ᇁᄋ ᅩ ᆫᄉ ᅳ ᆯᅧ ᅥ ᆼ ᄆᄇ ᆫᄉ ᅧ ᅮᄆ ᆫᄋ ᅡ ᆯᄃ ᅳ ᆨᄅ ᅩ ᆸᅧ ᅵ ᆫ ᄇᄉ ᅮ ᅩᄉ ᄅ ᅡᅭ ᆼ ᄋᄒ ᅡᄀ ᅩ, ᄉ ᆫᅢ ᅥ ᆨ ᄐᄃ ᆫᄇ ᅬ ᆫᄉ ᅧ ᅮᄃ ᆯᄆ ᅳ ᆫᄉ ᅡ ᅡᄋ ᆼᄒ ᅭ ᆫᄇ ᅡ ᆫᄉ ᅮ ᆫᄋ ᅡ ᅵᄏ ᆫᄌ ᅳ ᅮᄉ ᆼᄇ ᅥ ᆫᄆ ᅮ ᆫᄋ ᅡ ᅳᄅ ᅩᄇ ᆫᄋ ᅡ ᆼᄇ ᅳ ᆫᄉ ᅧ ᅮᄋ ᅦᄃ ᅢᄒ ᆫᄉ ᅡ ᆫᅧ ᅥ ᆼ ᄒᄆ ᅩᄒ ᆼᄋ ᅧ ᆯᄀ ᅳ ᅡᄌ ᆼᄒ ᅥ ᆫᄃ ᅡ ᅡ. ᆫᄀ ᅮ ᄇ ᅨᅥ ᆷ ᄌᄋ ᆫᄅ ᅳ ᅩᄀ ᅳᄀ ᅡᄂ ᆼᄃ ᅳ ᅩᄀ ᆷᅥ ᅥ ᆼ ᄌᄐ ᆼᄀ ᅩ ᅨᄅ ᆼᄋ ᅣ ᅴᄀ ᅭᄎ ᅡᄐ ᅡᄃ ᆼᄉ ᅡ ᆼ (cross-validation)ᄋ ᅥ ᆯᄐ ᅳ ᆼᄒ ᅩ ᅢᄉ ᆫᅢ ᅥ ᆨ ᄐᄒ ᅡᄀ ᅩ, ᄎ ᅬᄉ ᅩᄌ ᅦᄀ ᆸᄇ ᅩ ᆸ (least ᅥ squares method)ᄋ ᅳᄅ ᅩᄉ ᆫᄒ ᅥ ᆼᄆ ᅧ ᅩᄒ ᆼᄋ ᅧ ᅴᄆ ᅩᄉ ᅮᄅ ᆯᄎ ᅳ ᅮᄌ ᆼᄒ ᅥ ᆫᄃ ᅡ ᅡ. ᅮᅵ ᄋ ᄅᄂ ᆫᄋ ᅳ ᅵᄂ ᆫᄆ ᅩ ᆫᄋ ᅮ ᅦᄉ ᅥ Bair ᄃ ᆼ (2006)ᄋ ᅳ ᅵᄌ ᅦᄉ ᅵᄒ ᆫᄇ ᅡ ᆼᄇ ᅡ ᆸᄀ ᅥ ᅪᄂ ᆫᄃ ᅳ ᅡᄅ ᆫᄇ ᅳ ᆼᄇ ᅡ ᆸᄋ ᅥ ᅳᄅ ᅩ, ᄇ ᆫᄋ ᅡ ᆼᄇ ᅳ ᆫᄉ ᅧ ᅮᅪ ᄋᄋ ᅴᄀ ᆫᄀ ᅪ ᅨᄅ ᆯᄀ ᅳ ᅩᄅ ᅧᄒ ᅡ ᅧᄌ ᄋ ᅮᄉ ᆼᄇ ᅥ ᆫᄋ ᅮ ᅴᄀ ᆺᄉ ᅢ ᅮᄅ ᆯᄉ ᅳ ᆫᅢ ᅥ ᆨ ᄐᄒ ᅡᄂ ᆫᄇ ᅳ ᅦᄋ ᅵᄌ ᅵᄋ ᆫᅡ ᅡ ᆼ ᄇᄇ ᆸᄋ ᅥ ᆯᄌ ᅳ ᅦᄉ ᅵᄒ ᆫᄃ ᅡ ᅡ. ᄌ ᅮᄉ ᆼᄇ ᅥ ᆫᄒ ᅮ ᅬᄀ ᅱᄆ ᅩᄒ ᆼᄋ ᅧ ᅦᄉ ᅥᄇ ᆫᄋ ᅡ ᆼᄇ ᅳ ᆫᄉ ᅧ ᅮᅪ ᄋᄋ ᅴᄉ ᆯᅧ ᅥ ᆼ ᄆᄅ ᆨᄋ ᅧ ᅵ ᇁᄋ ᅩ ᄂ ᆫᅮ ᅳ ᄇᄇ ᆫᄌ ᅮ ᆸᄒ ᅵ ᆸᄌ ᅡ ᅮᄉ ᆼᄇ ᅥ ᆫᅳ ᅮ ᆯ ᄋᄉ ᆫᅢ ᅥ ᆨ ᄐᄒ ᅡᄂ ᆫᄇ ᅳ ᆼᄇ ᅡ ᆸᄋ ᅥ ᆫᄋ ᅳ ᆯᄇ ᅵ ᆫᄉ ᅡ ᆫᅧ ᅥ ᆼ ᄒᄆ ᅩᄒ ᆼᄋ ᅧ ᅦᄉ ᅥᄇ ᆫᄉ ᅧ ᅮᄅ ᆯᄉ ᅳ ᆫᄐ ᅥ ᆨᄒ ᅢ ᅡᄂ ᆫᄀ ᅳ ᆺᄀ ᅥ ᅪᄃ ᅡᄅ ᅳᄌ ᅵᄋ ᆭᄃ ᅡ ᅡ. ᄋ ᆯᄇ ᅵ ᆫ ᅡ ᆨᄋ ᅥ ᄌ ᅳᄅ ᅩᄇ ᆫᄉ ᅧ ᅮᄅ ᆯᄉ ᅳ ᆫᄐ ᅥ ᆨᄒ ᅢ ᅡᄂ ᆫᄃ ᅳ ᅡᄋ ᆼᅡ ᅣ ᆫ ᄒᄇ ᆼᄇ ᅡ ᆸᄋ ᅥ ᅵᄋ ᆻᄌ ᅵ ᅵᄆ ᆫ, ᄋ ᅡ ᅵᄂ ᆫᅮ ᅩ ᆫ ᄆᄋ ᅦᄉ ᅥᄆ ᅩᄉ ᅮᄅ ᆯᄎ ᅳ ᅮᄌ ᆼᄒ ᅥ ᅡᄂ ᆫᄇ ᅳ ᆼᄇ ᅡ ᆸᄋ ᅥ ᅳᄅ ᅩᄉ ᅡᄋ ᆼᄒ ᅭ ᅡᄂ ᆫᄇ ᅳ ᅦᄋ ᅵᄌ ᅵ ᆫᄎ ᅡ ᄋ ᅮᄅ ᆫᅳ ᅩ ᆯ ᄋᄇ ᅡᄐ ᆼᄋ ᅡ ᅳᄅ ᅩᄇ ᅦᄋ ᅵᄌ ᅳᄌ ᆼᄇ ᅥ ᅩᄀ ᅵᄌ ᆫ (Bayesian information criterion: BIC)ᄅ ᅮ ᆯᄀ ᅳ ᅩᄅ ᅧᄒ ᆫᄃ ᅡ ᅡ. ᅦᅵ ᄇ ᄋᄌ ᅵᄋ ᆫᄇ ᅡ ᆫᄉ ᅮ ᆨᄋ ᅥ ᅦᄉ ᅥᄇ ᅦᄋ ᅵᄌ ᅳᄋ ᆫᄌ ᅵ ᅡ (Bayes factor)ᄂ ᆫᄃ ᅳ ᅡᄋ ᆼᅡ ᅣ ᆫ ᄒᄐ ᆼᄀ ᅩ ᅨᄌ ᆨᄆ ᅥ ᅩᄒ ᆼᄋ ᅧ ᅴᄆ ᅩᄒ ᆼᄌ ᅧ ᆨᄒ ᅥ ᆸᅥ ᅡ ᆼ ᄉᄋ ᆯᄀ ᅳ ᆷᄌ ᅥ ᆼᄒ ᅥ ᆯᄄ ᅡ ᅢᄉ ᅡ ᆼᄒ ᅭ ᄋ ᆫᅡ ᅡ ᄃ. ᄆ ᆼᄉ ᅧ ᅵᄌ ᆨᄉ ᅥ ᅡᄌ ᆫᄇ ᅥ ᆫᄑ ᅮ ᅩᄅ ᆯᄀ ᅳ ᅩᄅ ᅧᄒ ᆫᄉ ᅡ ᅡᄒ ᅮᄇ ᆫᄑ ᅮ ᅩᄅ ᅩᄇ ᅮᄐ ᅥᄇ ᅦᄋ ᅵᄌ ᅳᄋ ᆫᄌ ᅵ ᅡᄅ ᆯᄀ ᅳ ᅨᄉ ᆫᄒ ᅡ ᅡᄂ ᆫᄀ ᅳ ᆺᄋ ᅥ ᆫᄋ ᅳ ᅥᄅ ᆸᄌ ᅧ ᅵᄋ ᆭᄃ ᅡ ᅡ. ᄀ ᅳᄅ ᅥ ᅡᄒ ᄂ ᆫᆯ ᅧ ᅵᄌ ᄉ ᆨᄋ ᅥ ᅳᄅ ᅩᄇ ᅦᄋ ᅵᄌ ᅳᄋ ᆫᄌ ᅵ ᅡᄅ ᆯᄀ ᅳ ᅨᄉ ᆫᄒ ᅡ ᅡᄀ ᅵᄉ ᆸᄀ ᅱ ᅦᄆ ᆫᄃ ᅡ ᅳᄂ ᆫᄉ ᅳ ᅡᄌ ᆫᄇ ᅥ ᆫᄑ ᅮ ᅩᄅ ᆯᄀ ᅳ ᅩᄅ ᅧᄒ ᅡᄋ ᅧᄆ ᅩᄒ ᆼᄋ ᅧ ᆯᄌ ᅳ ᆨᄒ ᅥ ᆸᄒ ᅡ ᅡᄂ ᆫᄇ ᅳ ᆼᄇ ᅡ ᆸᄋ ᅥ ᆫᄉ ᅳ ᆸᄌ ᅱ ᅵ ᆭᄋ ᅡ ᄋ ᅳᄆ ᅧ, ᄃ ᅡᄋ ᆼᅡ ᅣ ᆫ ᄒᄒ ᆼᄐ ᅧ ᅢᄋ ᅴᄉ ᅡᄌ ᆫᄇ ᅥ ᆫᄑ ᅮ ᅩᄅ ᅩᄇ ᅮᄐ ᅥᄌ ᆼᄒ ᅥ ᆨᄒ ᅪ ᆫᄇ ᅡ ᅦᄋ ᅵᄌ ᅳᄋ ᆫᄌ ᅵ ᅡᄅ ᆯᄀ ᅳ ᅨᄉ ᆫᄒ ᅡ ᅡᄂ ᆫᄀ ᅳ ᆺᄋ ᅥ ᆨᄉ ᅧ ᅵᄉ ᆸᄌ ᅱ ᅵᄋ ᆭᄃ ᅡ ᅡ. ᄀ ᅳᄅ ᅥᄆ ᅳᄅ ᅩ ᅦᄋ ᄇ ᅵᅵ ᄌᄋ ᆫᄎ ᅡ ᅮᄅ ᆫᄋ ᅩ ᅦᄉ ᅥᄂ ᆫᄇ ᅳ ᅦᄋ ᅵᄌ ᅳᄋ ᆫᄌ ᅵ ᅡᄋ ᅦᄀ ᆫᄉ ᅳ ᅡᄒ ᅡᄂ ᆫᄒ ᅳ ᆼᄐ ᅧ ᅢᄅ ᆯᄀ ᅳ ᆽᄂ ᅡ ᆫᄆ ᅳ ᅩᄒ ᆼᅥ ᅧ ᆫ ᄉᄐ ᆨᄐ ᅢ ᆼᄀ ᅩ ᅨᄅ ᆼᄋ ᅣ ᆯᄉ ᅳ ᅡᄋ ᆼᄒ ᅭ ᅡᄂ ᆫᄃ ᅳ ᅦ, ᄀ ᅳᄃ ᅢᄑ ᅭᄌ ᆨᄋ ᅥ ᆫ ᅵ ᆼᄀ ᅩ ᄐ ᅨᄅ ᆼᄋ ᅣ ᅵ Schwarz (1978)ᄀ ᅡᄌ ᅦᄉ ᅵᄒ ᆫ BICᄋ ᅡ ᅵᄃ ᅡ. BICᄋ ᅴᄌ ᆼᄌ ᅡ ᆷᄋ ᅥ ᆫ (1) ᄋ ᅳ ᅮᄃ ᅩᄒ ᆷᄉ ᅡ ᅮᄀ ᆹᄀ ᅡ ᅪᄆ ᅩᄒ ᆼᄌ ᅧ ᅡᄋ ᅲᄃ ᅩ, ᄀ ᅳᄅ ᅵᄀ ᅩᄑ ᅭ ᆫᄋ ᅩ ᄇ ᅴᄏ ᅳᄀ ᅵᄆ ᆫᄋ ᅡ ᅳᄅ ᅩᄉ ᆸᄀ ᅱ ᅦᄀ ᅨᄉ ᆫᅡ ᅡ ᆯ ᄒᄉ ᅮᄋ ᆻᄃ ᅵ ᅡᄂ ᆫᄀ ᅳ ᆺᄀ ᅥ ᅪ (2)ᄂ ᅢᄑ ᅩᄆ ᅩᄒ ᆼᄈ ᅧ ᆫᄋ ᅮ ᅡᄂ ᅵᄅ ᅡᄂ ᅢᄑ ᅩᄃ ᅬᄌ ᅵᄋ ᆭᄋ ᅡ ᆫᄆ ᅳ ᅩᄒ ᆼᄋ ᅧ ᅴᄌ ᆨᄒ ᅥ ᆸᄃ ᅡ ᅩᄇ ᅵᄀ ᅭ ᅦᄃ ᄋ ᅩᅡ ᄉᄋ ᆼᄒ ᅭ ᆯᄉ ᅡ ᅮᄋ ᆻᄃ ᅵ ᅡᄂ ᆫᄀ ᅳ ᆺ (Raftery, 1995)ᄋ ᅥ ᅵᄃ ᅡ. ᄇ ᆫᄆ ᅡ ᆫ BICᄋ ᅧ ᅴᄇ ᅦᄋ ᅵᄌ ᅳᄋ ᆫᄌ ᅵ ᅡᄅ ᅩᄋ ᅴᄀ ᆫᄉ ᅳ ᅡᄂ ᆫᄉ ᅳ ᅡᄌ ᆫᄇ ᅥ ᆫᄑ ᅮ ᅩᄋ ᅴᄒ ᅭᄀ ᅪ ᅡᄀ ᄀ ᅥᅴ ᄋᄋ ᆹᄀ ᅥ ᅥᄂ ᅡᄑ ᅭᄇ ᆫᄉ ᅩ ᅮᄀ ᅡᄏ ᆫᄀ ᅳ ᆼᄋ ᅧ ᅮᄃ ᅥᄒ ᅭᄀ ᅪᄌ ᆨᄋ ᅥ ᅵᄀ ᅩ, ᄑ ᅭᄇ ᆫᄉ ᅩ ᅮᄇ ᅩᄃ ᅡᄆ ᆭᄋ ᅡ ᆫᄉ ᅳ ᆯᄆ ᅥ ᆼᅧ ᅧ ᆫ ᄇᄉ ᅮᄅ ᆯᄑ ᅳ ᅩᄒ ᆷᄒ ᅡ ᆫᄌ ᅡ ᅡᄅ ᅭᅪ ᄋᄀ ᅩᄎ ᅡᄋ ᅯ ᅡᄅ ᄌ ᅭᅴ ᄋᄇ ᆫᄉ ᅧ ᅮᄉ ᆫᅢ ᅥ ᆨ ᄐᄆ ᅩᄒ ᆼᄋ ᅧ ᅦᄂ ᆫᄉ ᅳ ᅡᄋ ᆼᄒ ᅭ ᆯᄉ ᅡ ᅮᄋ ᆹᄃ ᅥ ᅡ. ᄀ ᅳᄅ ᅥᄂ ᅡᄋ ᅮᄅ ᅵᄀ ᅡᄌ ᅦᄉ ᅵᄒ ᆫᄆ ᅡ ᅩᄒ ᆼᄋ ᅧ ᆫᄀ ᅳ ᅩᄎ ᅡᄋ ᆫᄋ ᅯ ᅴᄌ ᅡᄅ ᅭᄅ ᆯᄃ ᅳ ᅡᄅ ᅮᄂ ᆫᄀ ᅳ ᆺᄃ ᅥ ᅩ ᅡᄂ ᄋ ᅵᅧ ᄆᄇ ᆫᄉ ᅧ ᅮᄉ ᆫᅢ ᅥ ᆨ ᄐᄋ ᅴᄆ ᅩᄒ ᆼᄋ ᅧ ᅵᄋ ᅡᄂ ᅵᄆ ᅳᄅ ᅩ, BICᄂ ᆫᄌ ᅳ ᅮᄋ ᅥᄌ ᆫᄌ ᅵ ᅡᄅ ᅭᄋ ᅦᄌ ᆨᄒ ᅥ ᆸᄀ ᅡ ᅡᄂ ᆼᄒ ᅳ ᆼᄃ ᅡ ᅡᄋ ᆼᄒ ᅣ ᆫᄆ ᅡ ᅩᄒ ᆼᄌ ᅧ ᆼᄀ ᅮ ᅡᄌ ᆼᄌ ᅡ ᆨᄒ ᅥ ᆸᄒ ᅡ ᆫᄆ ᅡ ᅩ ᆼᄋ ᅧ ᄒ ᆯᄉ ᅳ ᆫᅢ ᅥ ᆨ ᄐᄒ ᆯᄄ ᅡ ᅢᄉ ᅡᄋ ᆼᄒ ᅭ ᆯᄉ ᅡ ᅮᄋ ᆻᄂ ᅵ ᆫᄐ ᅳ ᆼᄀ ᅩ ᅨᄅ ᆼᄋ ᅣ ᅵᄅ ᅡᄒ ᆯᄉ ᅡ ᅮᄋ ᆻᄃ ᅵ ᅡ. ᆫᄂ ᅩ ᄇ ᆫᅮ ᅩ ᆫ ᄆᄋ ᅴᄀ ᅮᄉ ᆼᄋ ᅥ ᆫᄃ ᅳ ᅡᄋ ᆷᄀ ᅳ ᅪᄀ ᇀᄃ ᅡ ᅡ. 2ᄌ ᆯᄋ ᅥ ᅦᄉ ᅥᄂ ᆫᄌ ᅳ ᅮᄉ ᆼᄇ ᅥ ᆫᄒ ᅮ ᅬᄀ ᅱᄆ ᅩᄒ ᆼᄋ ᅧ ᅴᄀ ᅮᄉ ᆼᄋ ᅥ ᅦᄃ ᅢᄒ ᅢᄉ ᅥᄉ ᆯᅧ ᅥ ᆼ ᄆᄒ ᅡᄀ ᅩ, 3ᄌ ᆯᄋ ᅥ ᅦᄉ ᅥᄂ ᆫ ᅳ ᅩᄉ ᄆ ᅮᅴ ᄋᄉ ᅡᄌ ᆫᄇ ᅥ ᆫᄑ ᅮ ᅩᄋ ᅦᄃ ᅢᄒ ᆫᄀ ᅡ ᅡᄌ ᆼᄀ ᅥ ᅪᄉ ᅡᄒ ᅮᄇ ᆫᄑ ᅮ ᅩᄆ ᆾ BIC ᄀ ᅵ ᅨᄉ ᆫᄀ ᅡ ᅪᄌ ᆼᄋ ᅥ ᅦᄃ ᅢᄒ ᅢᄉ ᆯᅧ ᅥ ᆼ ᄆᄒ ᆫᄃ ᅡ ᅡ. ᄀ ᅳᄅ ᅵᄀ ᅩ 4ᄌ ᆯᄋ ᅥ ᅦᄉ ᅥᄂ ᆫᄉ ᅳ ᆯᄌ ᅵ ᅦᄌ ᅡ ᅭᄋ ᄅ ᅦᅥ ᆨ ᄌᄋ ᆼᄒ ᅭ ᆫᅧ ᅡ ᆯ ᄀᄀ ᅪᄋ ᅦᄃ ᅢᄒ ᅢᄉ ᆯᄑ ᅡ ᅧᄇ ᅩᄀ ᅩ, 5ᄌ ᆯᄋ ᅥ ᅦᄉ ᅥᄂ ᆫᄋ ᅳ ᅭᄋ ᆨᄀ ᅣ ᅪᄀ ᆯᄅ ᅧ ᆫᄋ ᅩ ᅳᄅ ᅩᄁ ᇀᅳ ᅳ ᆯ ᄋᄆ ᆽᄂ ᅢ ᆫᄃ ᅳ ᅡ..
(3) Bayesian PCA regression. 249. 2. 주성분 회귀모형 ᅬᅱ ᄒ ᄀᄆ ᅩᄒ ᆼᄋ ᅧ ᅦ ᄃ ᅢᄒ ᅡᄋ ᅧ pᄀ ᅢᄋ ᅴ ᄉ ᆯᅧ ᅥ ᆼ ᄆᄇ ᆫᄉ ᅧ ᅮᄃ ᆯᄀ ᅳ ᅪ ᅡ ᆫ ᄇᄋ ᆼᄇ ᅳ ᆫᄉ ᅧ ᅮ ᄆ ᅩᄃ ᅮ ᄑ ᅭᄌ ᆫᄒ ᅮ ᅪ (standardization)ᄒ ᆫ ᄀ ᅡ ᆹᄋ ᅡ ᆯ ᄉ ᅳ ᅡᄋ ᆼᄒ ᅭ ᆫᄃ ᅡ ᅡ. ᄌᄉ ᅮ ᆼᄇ ᅥ ᆫᄒ ᅮ ᅬᄀ ᅱᄆ ᅩᄒ ᆼᄋ ᅧ ᅴᄒ ᅬᄀ ᅱᄇ ᆫᄉ ᅧ ᅮᄋ ᆫᅥ ᅵ ᆯ 셔 ᆼ ᄆᄇ ᆫᄉ ᅧ ᅮᄃ ᆯᄋ ᅳ ᅴᄌ ᅮᄉ ᆼᄇ ᅥ ᆫᄋ ᅮ ᆯᄋ ᅳ ᆮᄀ ᅥ ᅵᄋ ᅱᄒ ᅡᄋ ᅧ, ᄌ ᅮᄉ ᆼᄇ ᅥ ᆫᅮ ᅮ ᆫ ᄇᄉ ᆨᄋ ᅥ ᆯᄉ ᅳ ᅵᄒ ᆼᄒ ᅢ ᅡᄂ ᆫᄃ ᅳ ᅢᄉ ᆫᅥ ᅵ ᆯ ᄉᄆ ᆼ ᅧ ᆫᄉ ᅧ ᄇ ᅮ ᅢ 혀 ᆼ ᆯ ᄅᄋ ᅴ ᄐ ᆨᄋ ᅳ ᅵᄀ ᆹᄇ ᅡ ᆫᄒ ᅮ ᅢ (singluar value decomposition)ᄅ ᆯ ᄐ ᅳ ᆼᄒ ᅩ ᆫ ᄌ ᅡ ᆨᄀ ᅵ ᅭᄉ ᆼᄋ ᅥ ᆯ ᄆ ᅳ ᆫᄌ ᅡ ᆨᄒ ᅩ ᅡᄂ ᆫ ᄒ ᅳ ᅬᄀ ᅱᄇ ᆫᄉ ᅧ ᅮᄅ ᆯ ᄎ ᅳ ᆽᄂ ᅡ ᆫ ᅳ ᅡ. ᄃ 2.1. 주성분분석과 특이값분해 ᅮᄉ ᄌ ᆼᄇ ᅥ ᆫᅮ ᅮ ᆫ ᄇᄉ ᆨᄋ ᅥ ᆫᄋ ᅳ ᅧᄅ ᅥᄀ ᅢᄋ ᅴᄇ ᆫᄉ ᅧ ᅮᄅ ᅩᄋ ᆮᄋ ᅥ ᅥᄌ ᆫᄃ ᅵ ᅡᄇ ᆫᄅ ᅧ ᆼᄌ ᅣ ᅡᄅ ᅭᄋ ᅦᄃ ᅢᄒ ᅢ, ᄀ ᆼᅮ ᅩ ᆫ ᄇᄉ ᆫᄀ ᅡ ᅮᄌ ᅩᄅ ᆯᄇ ᅳ ᆫᄉ ᅧ ᅮᄃ ᆯᄋ ᅳ ᅴᄉ ᆫᅧ ᅥ ᆼ 혀 ᆯ ᄀᄒ ᆸᄉ ᅡ ᆨᄋ ᅵ ᆫᄌ ᅵ ᅮ ᄉᄇ ᆼ ᅥ ᆫᅳ ᅮ ᄋᄅ ᅩᄉ ᆯᅧ ᅥ ᆼ ᄆᄒ ᅡᄀ ᅩᄌ ᅡᄒ ᅡᄂ ᆫᄌ ᅳ ᆸᄀ ᅥ ᆫᄇ ᅳ ᆼᄇ ᅡ ᆸᄋ ᅥ ᅳᄅ ᅩᄀ ᆼᅮ ᅩ ᆫ ᄇᄉ ᆫᄒ ᅡ ᆼᅧ ᅢ ᆯ ᄅᄋ ᅴᄉ ᅳᄑ ᆨᄐ ᅦ ᅳᄅ ᆷᄇ ᅥ ᆫᄒ ᅮ ᅢ (spectral decomposition)ᄅ ᆯᄐ ᅳ ᆼᄒ ᅩ ᅡ T 1 ᅧᄋ ᄋ ᆮᄂ ᅥ ᆫᄃ ᅳ ᅡ. ᄋ ᆯᄇ ᅵ ᆫᄌ ᅡ ᆨᄋ ᅥ ᅳᄅ ᅩᄑ ᅭᄌ ᆫᄒ ᅮ ᅪᄃ ᆫᄉ ᅬ ᆯᅧ ᅥ ᆼ 며 ᆫ ᄇᄉ ᅮᄒ ᆼᅧ ᅢ ᆯ ᄅ Xᄋ ᅴᄀ ᆼᅮ ᅩ ᆫ ᄇᄉ ᆫᄒ ᅡ ᆼᅧ ᅢ ᆯ ᄅ (covariance matrix)ᄋ ᆫ n−1 X Xᄋ ᅳ ᅪ ᇀᄋ ᅡ ᄀ ᅵᅨ ᄀᄉ ᆫᄃ ᅡ ᅬᄀ ᅩ, ᄋ ᅵᄀ ᆼᅮ ᅩ ᆫ ᄇᄉ ᆫᄒ ᅡ ᆼᄅ ᅢ ᆯᄋ ᅧ ᆫᄋ ᅳ ᆼᅡ ᅣ ᆫ ᄇᄌ ᆼᄎ ᅥ ᅵ (positive semidefinite)ᄅ ᅩᄃ ᅢᄎ ᆼ (symmetric)ᄉ ᅵ ᆼᄋ ᅥ ᆯᄆ ᅳ ᆫᄌ ᅡ ᆨᄒ ᅩ ᆫᄃ ᅡ ᅡ. ᆼᄅ ᅢ ᄒ ᆯ X T Xᄅ ᅧ ᆯᄐ ᅳ ᆨᄋ ᅳ ᅵᄀ ᆹᄇ ᅡ ᆫᄒ ᅮ ᅢᄂ ᆫᄃ ᅳ ᅡᄋ ᆷᄀ ᅳ ᅪᄀ ᇀᄋ ᅡ ᅵᄑ ᅭᄒ ᆫᄒ ᅧ ᆯᄉ ᅡ ᅮᄋ ᆻᄃ ᅵ ᅡ. X T X = V ΓV T . ᄋᄀ ᅧ ᅵᅦ ᄋᄉ ᅥ V ᄋ ᆯᄇ ᅧ ᆨᄐ ᅦ ᅥᄃ ᆯᄋ ᅳ ᆫ X T Xᄋ ᅳ ᅴᄀ ᅩᄋ ᅲᄇ ᅦ ᆨᄐ ᅥ (eigenvector)ᄅ ᅩᄋ ᅵᄅ ᅮᄋ ᅥᄌ ᆫᄒ ᅵ ᆼᅧ ᅢ ᆯ ᄅᄋ ᅵᄀ ᅩ, Γᄂ ᆫᄃ ᅳ ᅢᄀ ᆨᄉ ᅡ ᆼᄇ ᅥ ᆫᄋ ᅮ ᅵᄀ ᆼᅮ ᅩ ᆫ ᄇ ᆫᄒ ᅡ ᄉ ᆼᄅ ᅢ ᆯᄋ ᅧ ᅴᄀ ᅩᄋ ᅲᄀ ᆹ(eigenvalue)ᄋ ᅡ ᆫᄃ ᅵ ᅢᄀ ᆨᆼ ᅡ ᄒ ᅢᅧ ᆯ ᄅᄋ ᅵᄃ ᅡ. ᄋ ᅵᄅ ᆯᄒ ᅳ ᆯᄋ ᅪ ᆼᄒ ᅭ ᅡᄋ ᅧ n×p ᄉ ᆯᅧ ᅥ ᆼ 며 ᆫ ᄇᄉ ᅮᄒ ᆼᄅ ᅢ ᆯ Xᄋ ᅧ ᅦᄃ ᅢᄒ ᆫᄌ ᅡ ᅮᄉ ᆼᄇ ᅥ ᆫ ᅮ ᆫᄉ ᅮ ᄇ ᆨᅳ ᅥ ᆫ ᄋᄃ ᅡᄋ ᆷᄀ ᅳ ᅪᄀ ᇀᄋ ᅡ ᅵᄑ ᅭᄒ ᆫᄒ ᅧ ᆯᄉ ᅡ ᅮᄋ ᆻᄃ ᅵ ᅡ. T = XV ᄋᄀ ᅧ ᅵᅦ ᄋᄉ ᅥ Vᄂ ᆫ p×p ᄀ ᅳ ᅡᄌ ᆼᄎ ᅮ ᅵᄒ ᆼᅧ ᅢ ᆯ ᄅ (weight matrix)ᄅ ᅩᄋ ᆯᄇ ᅧ ᆨᄐ ᅦ ᅥᄃ ᆯᄋ ᅳ ᆫ X T Xᄋ ᅳ ᅴᄀ ᅩᄋ ᅲᄇ ᆨᄐ ᅦ ᅥᄃ ᆯᄅ ᅳ ᅩᄋ ᅵᄅ ᅮᄋ ᅥᄌ ᆫᄒ ᅵ ᆼ ᅢ ᆯᄋ ᅧ ᄅ ᅵᄀ ᅩ, ᄋ ᅵᄄ ᅢᄃ ᅢᄋ ᆼᄒ ᅳ ᅡᄂ ᆫᄀ ᅳ ᅩᄋ ᅲᄀ ᆹᄋ ᅡ ᅴᄌ ᅦᄀ ᆸᆫ ᅩ ᄀ ᅳᅳ ᆯ 오 ᆸ ᄀᄒ ᆫ Vᄋ ᅡ ᅴᄋ ᆯᄋ ᅧ ᆯᄌ ᅳ ᅮᄉ ᆼᄇ ᅥ ᆫᄌ ᅮ ᆨᄌ ᅥ ᅢᄀ ᆹ (principal component loadᅡ ing)ᄋ ᅵᄅ ᅡᄒ ᆫᄃ ᅡ ᅡ. ᄌ ᆨ, ᄌ ᅳ ᅮᄉ ᆼᄇ ᅥ ᆫᄌ ᅮ ᆨᄌ ᅥ ᅢᄀ ᆹᄋ ᅡ ᆫ ᅳ P = V Γ1/2 ᅩ ᄅ XT X = P T P ᅩᄑ ᄅ ᅭᄒ ᆫᄒ ᅧ ᆯᄉ ᅡ ᅮᄋ ᆻᄃ ᅵ ᅡ. ᆨᄋ ᅳ ᄐ ᅵᄀ ᆹᄇ ᅡ ᆫᄒ ᅮ ᅢᄂ ᆫᄀ ᅳ ᅩᄋ ᅲᄀ ᆹᄇ ᅡ ᆫᄒ ᅮ ᅢᄎ ᅥᄅ ᆷᄒ ᅥ ᆼᅧ ᅢ ᆯ ᄅᄋ ᆯᄃ ᅳ ᅢᄀ ᆨᄒ ᅡ ᅪᄒ ᅡᄂ ᆫᄇ ᅳ ᆼᄇ ᅡ ᆸᄋ ᅥ ᅳᄅ ᅩᄒ ᆼᅧ ᅢ ᆯ ᄅᄋ ᅵᄌ ᆼᄇ ᅥ ᆼᄒ ᅡ ᆼᅧ ᅢ ᆯ ᄅᄋ ᅵᄋ ᅡᄂ ᅵᄋ ᅥᄃ ᅩᄆ ᅩᄃ ᆫ n×p ᅳ ᆼᄅ ᅢ ᄒ ᆯᅦ ᅧ ᄋᄃ ᅢᄒ ᅡᄋ ᅧᄌ ᆨᄋ ᅥ ᆼᄀ ᅭ ᅡᄂ ᆼᄒ ᅳ ᆫᄃ ᅡ ᅡ. ᄋ ᆯᄇ ᅵ ᆫᄌ ᅡ ᆨᄋ ᅥ ᅳᄅ ᅩ n×p ᄉ ᆯᅧ ᅥ ᆼ 며 ᆫ ᄇᄉ ᅮᄒ ᆼᅧ ᅢ ᆯ ᄅ Xᄋ ᅦᄃ ᅢᄒ ᆫᄐ ᅡ ᆨᄋ ᅳ ᅵᄀ ᆹᄇ ᅡ ᆫᄒ ᅮ ᅢ(SVD)ᄂ ᆫᄃ ᅳ ᅡᄋ ᆷᄀ ᅳ ᅪ ᇀᄋ ᅡ ᄀ ᅵᄌ ᆼᄋ ᅥ ᅴᄃ ᆫᄃ ᅬ ᅡ. X = U ΣV T . ᄋᄀ ᅧ ᅵᅦ ᄋᄉ ᅥ Uᄂ ᆫ XX T ᄅ ᅳ ᆯᄀ ᅳ ᅩᄋ ᅲᄀ ᆹᄇ ᅡ ᆫᄒ ᅮ ᅢᄒ ᅢᄉ ᅥᄋ ᆮᄋ ᅥ ᅥᄌ ᆫ n×p ᄌ ᅵ ᆨᄀ ᅵ ᅭᄌ ᆼᄀ ᅥ ᅲᄒ ᆼᄅ ᅢ ᆯ (orthonormal matrix)ᄅ ᅧ ᅩ Uᄋ ᅴᄋ ᆯ ᅧ T ᆨᄐ ᅦ ᄇ ᅥᅳ ᆯ 드 ᆯ ᄋ Xᄋ ᅴ ᄋ ᆫᄍ ᅬ ᆨᄇ ᅩ ᅵᄌ ᆼᄎ ᅥ ᆨᄒ ᅵ ᆼᅧ ᅢ ᆯ ᄅ (left singular vector)ᄋ ᅵᄅ ᅡ ᄇ ᅮᄅ ᆫᄃ ᅳ ᅡ. ᄄ ᅩᄒ ᆫ Vᄂ ᅡ ᆫ X Xᄅ ᅳ ᆯ ᄀ ᅳ ᅩᄋ ᅲᄀ ᆹᄇ ᅡ ᆫᄒ ᅮ ᅢᄒ ᅢ ᅥᄋ ᄉ ᆮᅥ ᅥ ᄋᄌ ᆫ p×p ᄌ ᅵ ᆨᄀ ᅵ ᅭᄌ ᆼᄀ ᅥ ᅲᄒ ᆼᄅ ᅢ ᆯᄅ ᅧ ᅩᄉ ᅥV ᄋ ᅴᄋ ᆯᄇ ᅧ ᆨᄐ ᅦ ᅥᄃ ᆯᅳ ᅳ ᆯ ᄋ Xᄋ ᅴᄋ ᅩᄅ ᆫᄍ ᅳ ᆨᄇ ᅩ ᅵᄌ ᆼᄎ ᅥ ᆨᄒ ᅵ ᆼᅧ ᅢ ᆯ ᄅ (right singular vector)ᄋ ᅵ ᅡᄇ ᄅ ᅮᅳ ᆫ ᄅᄃ ᅡ. ᄀ ᅳᄅ ᅵᄀ ᅩ Σᄂ ᆫ XX T , X T Xᄅ ᅳ ᆯᄀ ᅳ ᅩᄋ ᅲᄀ ᆹᄇ ᅡ ᆫᄒ ᅮ ᅢᄒ ᅢᄉ ᅥᄂ ᅡᄋ ᅩᄂ ᆫᄀ ᅳ ᅩᄋ ᅲᄀ ᆹᄃ ᅡ ᆯᄋ ᅳ ᅴᄌ ᅦᄀ ᆸᄀ ᅩ ᆫ (square root) ᄃ ᅳ ᅢ ᆨᄋ ᅡ ᄀ ᆫᅩ ᅯ ᄉᄅ ᅩᄒ ᅡᄂ ᆫ n×p ᄌ ᅳ ᆨᄉ ᅵ ᅡᄀ ᆨᄃ ᅡ ᅢᄀ ᆨᄒ ᅡ ᆼᄅ ᅢ ᆯᄅ ᅧ ᅩᄀ ᅳᄃ ᅢᄀ ᆨᄋ ᅡ ᆫᄉ ᅯ ᅩᄃ ᆯᄋ ᅳ ᆯ X ᄐ ᅳ ᆨᄋ ᅳ ᅵᄀ ᆹ(singular value)ᄋ ᅡ ᅵᄅ ᅡᄇ ᅮᄅ ᆫᄃ ᅳ ᅡ. ᄄ ᅩ XX T ᄋ ᅪ X T Xᄋ ᅴᄀ ᅩᄋ ᅲᄀ ᆹᄃ ᅡ ᆯᄋ ᅳ ᆫᄇ ᅳ ᅵᄋ ᆷ (nonnegative)ᄋ ᅳ ᅵᄆ ᅧ 0ᄋ ᅵᄋ ᅡᄂ ᆫᄀ ᅵ ᅩᄋ ᅲᄀ ᆹᄃ ᅡ ᆯᄋ ᅳ ᆫᄉ ᅳ ᅥᄅ ᅩᄃ ᆼᄋ ᅩ ᆯᄒ ᅵ ᅡᄃ ᅡ..
(4) 250. Minjung Kyung. ᄐᄋ ᆨ ᅳ ᅵᄀ ᆹᄇ ᅡ ᆫᄒ ᅮ ᅢᅪ ᄋᄌ ᅮᄉ ᆼᄇ ᅥ ᆫᅮ ᅮ ᆫ ᄇᄉ ᆨᄀ ᅥ ᅪᄋ ᅴᄀ ᆫᄀ ᅪ ᅨᄅ ᆯᄉ ᅳ ᆯᄆ ᅥ ᆼᄒ ᅧ ᅡᄀ ᅵᄋ ᅱᄒ ᅢ, ᄒ ᆼᅧ ᅢ ᆯ ᄅᄋ ᅴᄋ ᆫᄉ ᅵ ᅮᄇ ᆫᄒ ᅮ ᅢᄀ ᆫᄌ ᅪ ᆷᄋ ᅥ ᅦᄉ ᅥᄒ ᆼᄅ ᅢ ᆯ X T Xᄅ ᅧ ᆯᄐ ᅳ ᆨᄋ ᅳ ᅵ ᆹᄇ ᅡ ᄀ ᆫᅢ ᅮ ᄒᄋ ᅴᄒ ᆼᄐ ᅧ ᅢᄅ ᅩᄃ ᅡᄉ ᅵᄑ ᅭᄒ ᆫᄒ ᅧ ᅢᄇ ᅩᄆ ᆫ, ᅧ. XT X. =. V ΣU T U ΣV T = V Σ2 V T. =. V ΓV T. ᅪᄀ ᄋ ᇀᅳ ᅡ ᆫ ᄋᄀ ᆫᄀ ᅪ ᅨᄀ ᅡᄉ ᆼᄅ ᅥ ᆸᄃ ᅵ ᆷᄋ ᅬ ᆯᄋ ᅳ ᆯᄉ ᅡ ᅮᄋ ᆻᄃ ᅵ ᅡ. 2.2. 주성분 회귀모형 Kᄀ ᅢᄋ ᅴᄌ ᅮᄉ ᆼᄇ ᅥ ᆫᄆ ᅮ ᆫᄋ ᅡ ᆯᄉ ᅳ ᅡᄋ ᆼᄒ ᅭ ᅡᄋ ᅧᄉ ᆯᄆ ᅥ ᆼᅧ ᅧ ᆫ ᄇᄉ ᅮᄒ ᆼᅧ ᅢ ᆯ ᄅ Xᄅ ᆯᄑ ᅳ ᅭᄒ ᆫᄒ ᅧ ᅡᄆ ᆫ, ᅧ X = U L ΣL V TL ≡ F A ᄋᄀ ᅪ ᇀᄀ ᅡ ᅩ, F = U L ΣL ᄂ ᆫ n×K ᄋ ᅳ ᆫᄌ ᅵ ᅡᄒ ᆼᅧ ᅢ ᆯ ᄅ (factor matrix)ᄋ ᅵᄀ ᅩ A = V TL ᄂ ᆫ K ×p ᄐ ᅳ ᆨᄋ ᅳ ᅵᄀ ᆹᄇ ᅡ ᆫᄒ ᅮ ᅢᄌ ᆨᄌ ᅥ ᅢᄀ ᆹᄒ ᅡ ᆼ ᅢ T T 2 ᅳᄅ ᅩ ΣL Σᄋ ᅦᄉ ᅥᄋ ᆼᄋ ᅣ ᅴᄐ ᆨᄋ ᅳ ᅵᄀ ᆹᄋ ᅡ ᆯᄀ ᅳ ᆽᄂ ᅡ ᆫᄋ ᅳ ᆫ ᅯ ᆯᄋ ᅧ ᄅ ᅵᄃ ᆫᄃ ᅬ ᅡ. ᄋ ᅵᄄ ᅢ K < min (n, p)ᄋ ᅵᄀ ᅩ AA = I, F F = ΣL ᄋ ᅩᄃ ᄉ ᆯᄋ ᅳ ᆯᄏ ᅳ ᆫᄀ ᅳ ᆹᄋ ᅡ ᅦᄉ ᅥᄌ ᆨᄋ ᅡ ᆫᄀ ᅳ ᆹᄋ ᅡ ᅳᄅ ᅩᄉ ᆫᄉ ᅮ ᅥᄒ ᅪᄒ ᅡᄋ ᅧᄃ ᅢᄀ ᆨᄋ ᅡ ᆫᄉ ᅯ ᅩᄅ ᅩᄒ ᅡᄂ ᆫ K ×K ᄒ ᅳ ᆼᄅ ᅢ ᆯᄋ ᅧ ᅵᄃ ᅡ. ᄌ ᆨ, Σ2 ᄋ ᅳ ᆫ Γᄋ ᅳ ᅦᄉ ᅥᄋ ᆼᄋ ᅣ ᅴᄀ ᅩ ᅲᄀ ᄋ ᆹᄋ ᅡ ᆯᄀ ᅳ ᆽᄂ ᅡ ᆫᄋ ᅳ ᆫᄉ ᅯ ᅩᄃ ᆯᅳ ᅳ ᆯ ᄋᄏ ᆫᄀ ᅳ ᆹᄋ ᅡ ᅦᄉ ᅥᄌ ᆨᄋ ᅡ ᆫᄀ ᅳ ᆹᄋ ᅡ ᅳᄅ ᅩᄉ ᆫᄉ ᅮ ᅥᄒ ᅪᄒ ᅡᄋ ᅧᄃ ᅢᄀ ᆨᄋ ᅡ ᆫᄉ ᅯ ᅩᄅ ᅩᄒ ᅡᄂ ᆫ K×K ᄒ ᅳ ᆼᅧ ᅢ ᆯ ᄅᄋ ᅵᅬ ᄃᄂ ᆫᄀ ᅳ ᆺᄋ ᅥ ᅵᄃ ᅡ. ᅵᄂ ᄋ ᆫᄎ ᅳ ᅡᄋ ᆫᄎ ᅯ ᆨᄉ ᅮ ᅩᅪ ᄋᄋ ᆫᄀ ᅧ ᆯᄃ ᅧ ᅬᄂ ᆫᅮ ᅳ ᆫ ᄆᄌ ᅦᄋ ᅵᄆ ᅧ, ᄉ ᆯᄆ ᅥ ᆼᅧ ᅧ ᆫ ᄇᄉ ᅮᄃ ᆯᄉ ᅳ ᅡᄋ ᅵᄋ ᅦᄌ ᆫᄌ ᅩ ᅢᄒ ᅡᄂ ᆫᄃ ᅳ ᅡᄌ ᆼᄀ ᅮ ᆼᄉ ᅩ ᆫᅥ ᅥ ᆼ ᄉᄆ ᆫᄌ ᅮ ᅦᄅ ᆯᄉ ᅳ ᅥᄅ ᅩᄌ ᆨᄀ ᅵ ᅭᄀ ᅡᅬ ᄃᄂ ᆫ ᅳ ᅮᄉ ᄌ ᆼᄇ ᅥ ᆫᅳ ᅮ ᆯ ᄋᄒ ᅬᄀ ᅱᄇ ᆫᄉ ᅧ ᅮᄅ ᅩᄉ ᅡᄋ ᆼᄒ ᅭ ᅡᄋ ᅧᄒ ᅢᄀ ᆯᄒ ᅧ ᆫᄇ ᅡ ᆼᄇ ᅡ ᆸᄋ ᅥ ᅵᄃ ᅡ. ᆯᄋ ᅵ ᄀ ᅵ nᄋ ᆫᄇ ᅵ ᆫᄋ ᅡ ᆼᄇ ᅳ ᆨᄐ ᅦ ᅥ yᄋ ᅦᄃ ᅢᄒ ᆫᄋ ᅡ ᆯᄇ ᅵ ᆫᄌ ᅡ ᆨᅥ ᅥ ᆫ 셔 ᆼ ᄒᄆ ᅩᄒ ᆼᄋ ᅧ ᅦᄉ ᅥ y = Xβ + ϵ = F θ + ϵ ᄋᄆ ᅵ ᅧ, θ = Aβᄂ ᆫᄋ ᅳ ᆫᄌ ᅵ ᅡᄇ ᆫᄉ ᅧ ᅮᄃ ᆯᄋ ᅳ ᅴᄀ ᆯᄋ ᅵ ᅵK ᄒ ᅬᄀ ᅱᄆ ᅩᄉ ᅮᄋ ᅵᄆ ᅧ, pᄀ ᅢᄋ ᅴᄆ ᅩᄉ ᅮᄅ ᆯ Kᄀ ᅳ ᅢᄅ ᅩᄌ ᆯᄋ ᅮ ᆫᄒ ᅵ ᅭᄀ ᅪᄅ ᆯᄉ ᅳ ᆯᅧ ᅥ ᆼ ᄆᄒ ᅡᄂ ᆫᄇ ᅳ ᆨᄐ ᅦ ᅥ ᅵᄃ ᄋ ᅡ. ᄌ ᆨ, ᄌ ᅳ ᅮᄉ ᆼᄇ ᅥ ᆫᄒ ᅮ ᅬᄀ ᅱᄆ ᅩᄒ ᆼᄋ ᅧ ᆫᄋ ᅳ ᅩᄎ ᅡᄒ ᆼ ϵᄋ ᅡ ᅦᅥ ᄌᄀ ᆼ ᅲᄉ ᆼ M V Nn 0, σ 2 I ᄋ ᅥ ᆯᄀ ᅳ ᅡᄌ ᆼᄒ ᅥ ᆫ ᅡ y = Fθ + ϵ. (2.1). ᄋᄉ ᆯ ᅳ ᅡᅭ ᆼ ᄋᄒ ᆫᄃ ᅡ ᅡ. ᄋ ᆫᄒ ᅯ ᅬᄀ ᅱᄆ ᅩᄉ ᅮ (original regression parameter) βᄋ ᅦᄃ ᅢᄒ ᆫᄎ ᅡ ᅮᄅ ᆫᄋ ᅩ ᆫᄌ ᅳ ᅮᄉ ᆼᄇ ᅥ ᆫᄒ ᅮ ᅬᄀ ᅱᄆ ᅩᄒ ᆼᄋ ᅧ ᅦᄉ ᅥ θᄋ ᆯ ᅳ ᅮᄌ ᄎ ᆼᄒ ᅥ ᆫᄒ ᅡ ᅮᄋ ᆯᄇ ᅵ ᆫᄌ ᅡ ᆨᄋ ᅥ ᆫᄎ ᅵ ᅬᄉ ᅩᄂ ᅩᄅ ᆷᄋ ᅳ ᆨᅧ ᅧ ᆫ ᄇᄒ ᆫ (least-norm inverse)ᄋ ᅪ ᆯᄀ ᅳ ᅩᄅ ᅧᄒ ᆫ β = AT θᄅ ᅡ ᆯᄒ ᅳ ᆯᄋ ᅪ ᆼᄒ ᅭ ᆯᄉ ᅡ ᅮᄋ ᆻᄃ ᅵ ᅡ. ᄋ ᅵᄅ ᅥ ᆫᄌ ᅡ ᄒ ᅮᄉ ᆼᄇ ᅥ ᆫᄒ ᅮ ᅬᄀ ᅱᄆ ᅩᄒ ᆼᄋ ᅧ ᅴᄎ ᅮᄅ ᆫᄋ ᅩ ᅳᄅ ᅩᄂ ᆫᄇ ᅳ ᅦᄋ ᅵᄌ ᅵᄋ ᆫᄎ ᅡ ᅮᄌ ᆼᄇ ᅥ ᆸᄋ ᅥ ᆯᄉ ᅳ ᅡᄋ ᆼᄒ ᅭ ᅡᄆ ᅧ, ᄆ ᅩᄉ ᅮᄋ ᅦᄃ ᅢᄒ ᆫᄉ ᅡ ᅡᄌ ᆫᄇ ᅥ ᆫᄑ ᅮ ᅩᄆ ᆾᄉ ᅵ ᅡᄒ ᅮᄇ ᆫᄑ ᅮ ᅩᄋ ᅦ ᅢᄒ ᄃ ᆫᅡ ᅡ ᄌᄉ ᅦᄒ ᆫᄀ ᅡ ᅪᄌ ᆼᄋ ᅥ ᆫᄃ ᅳ ᅡᄋ ᆷᄌ ᅳ ᆼᄋ ᅡ ᅦᄉ ᅥᄉ ᆯᄒ ᅮ ᆫᄃ ᅡ ᅡ. ᆯᄇ ᅵ ᄋ ᆫᄌ ᅡ ᆨᄋ ᅥ ᅳᄅ ᅩ ᄌ ᅮᄉ ᆼᄇ ᅥ ᆫ ᅮ ᅮ ᆫ ᄇᄉ ᆨᄋ ᅥ ᅦᄉ ᅥ ᄌ ᆨᅥ ᅥ ᆯ ᄌᄒ ᆫ ᄌ ᅡ ᅮᄉ ᆼᄇ ᅥ ᆫᄋ ᅮ ᅴ ᄀ ᆺᄉ ᅢ ᅮᄋ ᆫ Kᄅ ᅵ ᆯ ᄉ ᅳ ᆫᄐ ᅥ ᆨᄒ ᅢ ᅡᄂ ᆫ ᄇ ᅳ ᆼᄇ ᅡ ᆸᄋ ᅥ ᅦ ᄃ ᅢᄒ ᅢ Jolliffe (1982)ᄂ ᆫ ᅳ ᅡᄋ ᄃ ᆷᄀ ᅳ ᅪ ᄀ ᇀᄋ ᅡ ᆫ ᄉ ᅳ ᅦ ᄇ ᆼᄇ ᅡ ᆸᄋ ᅥ ᅳᄅ ᅩ ᄇ ᆫᄅ ᅮ ᅲᄒ ᅡᄋ ᆻᄃ ᅧ ᅡ. ᄎ ᆺᄍ ᅥ ᅢᄂ ᆫ ᄌ ᅳ ᅮᄀ ᆫᄌ ᅪ ᆨᄋ ᅥ ᆫ ᄇ ᅵ ᆼᄇ ᅡ ᆸᄋ ᅥ ᅳᄅ ᅩ ᄉ ᅳᄏ ᅳᄅ ᅵ ᄀ ᅳᄅ ᅢᄑ ᅳ (scree plot), ᄌ ᆫ ᅥ ᅦᄇ ᄎ ᆫᅵ ᅧ ᄋᄋ ᅦᄋ ᅴ ᄀ ᆼᄒ ᅩ ᆫᄃ ᅥ ᅩ (percentage of total variance) ᄃ ᆼᄋ ᅳ ᅵ ᄃ ᅢᄑ ᅭᄌ ᆨᄋ ᅥ ᆫ ᄋ ᅵ ᅨᄋ ᅵᄃ ᅡ. ᄃ ᆯᄍ ᅮ ᅢᄂ ᆫ ᅮ ᅳ ᆫ ᄇᄑ ᅩᄀ ᅵᄇ ᆫ ᄀ ᅡ ᆷᅥ ᅥ ᆼ ᄌ ᆸ (distribution-based test tool)ᄅ ᅥ ᄇ ᅩ Bartlettᄋ ᅴ ᄀ ᆷᅥ ᅥ ᆼ 저 ᆸ ᄇᄋ ᅵ ᄃ ᅢᄑ ᅭᄌ ᆨᄋ ᅥ ᆫ ᄋ ᅵ ᅨᄋ ᅵᄃ ᅡ. ᄆ ᅡᄌ ᅵᄆ ᆨᄋ ᅡ ᅳᄅ ᅩ, ᄀ ᅭᄎ ᅡᄐ ᅡᄃ ᆼᄉ ᅡ ᆼ ᅥ (cross-validation)ᄀ ᅪᄀ ᇀᄋ ᅡ ᆫᅮ ᅳ ᆫ ᄇᄉ ᆨᄌ ᅥ ᆨᄀ ᅥ ᅪᄌ ᆼᄋ ᅥ ᆯᄋ ᅳ ᅵᄋ ᆼᄒ ᅭ ᅡᄂ ᆫᄇ ᅳ ᆼᄇ ᅡ ᆸᄋ ᅥ ᅵᄃ ᅡ. ᄀ ᆨᅡ ᅡ ᆼ ᄇᄇ ᆸᄃ ᅥ ᆯᄋ ᅳ ᆫᄌ ᅳ ᆼᅡ ᅡ ᆫ ᄃᄌ ᆷᄋ ᅥ ᅵᄌ ᆫᄌ ᅩ ᅢᄒ ᅡᄋ ᅧ, ᄋ ᅥᄄ ᆫᄒ ᅥ ᆫ ᅡ ᆼᄇ ᅡ ᄇ ᆸᅵ ᅥ ᄋᄀ ᅡᄌ ᆼᄌ ᅡ ᇂᄃ ᅩ ᅡᄀ ᅩᄒ ᆯᄉ ᅡ ᅮᄂ ᆫᄋ ᅳ ᆹᄃ ᅥ ᅡ. ᄀ ᅳᄅ ᅥᄂ ᅡᄋ ᅵᄋ ᆫᄀ ᅧ ᅮᄋ ᅦᄉ ᅥᄋ ᅮᄅ ᅵᄂ ᆫᄌ ᅳ ᅮᄉ ᆼᄇ ᅥ ᆫᄋ ᅮ ᅴᄀ ᆺᄉ ᅢ ᅮᄅ ᆯᄉ ᅳ ᆫᄐ ᅥ ᆨᄒ ᅢ ᅡᄂ ᆫᄇ ᅳ ᆼᄇ ᅡ ᆸᄋ ᅥ ᅳᄅ ᅩᄆ ᅩᄉ ᅮ ᆯᄎ ᅳ ᄅ ᅮᄌ ᆼᄒ ᅥ ᅡᄂ ᆫᄀ ᅳ ᅪᄌ ᆼᄋ ᅥ ᅦᄉ ᅥᄀ ᅮᄒ ᅢᄌ ᅵᄂ ᆫᄇ ᅳ ᅦᄋ ᅵᄌ ᅳᄌ ᆼᄇ ᅥ ᅩᄀ ᅵᄌ ᆫᅳ ᅮ ᆯ ᄋᄉ ᅡᄋ ᆼᄒ ᅭ ᅡᄋ ᅧ, ᄇ ᆫᄋ ᅡ ᆼᄇ ᅳ ᆫᄉ ᅧ ᅮᅪ ᄋᄋ ᅴᄉ ᆯᅧ ᅥ ᆼ ᄆᄅ ᆨᄋ ᅧ ᅵᄂ ᇁᄋ ᅩ ᆫᄇ ᅳ ᅮᄇ ᆫᄌ ᅮ ᆸᄒ ᅵ ᆸᄌ ᅡ ᅮ ᆼᄇ ᅥ ᄉ ᆫᄋ ᅮ ᆯᄉ ᅳ ᆫᅢ ᅥ ᆨ ᄐᄒ ᆫᄃ ᅡ ᅡ..
(5) 251. Bayesian PCA regression. 3. 베이지안 추정법 3.1. 축소 사전분포 ᅮᄉ ᄌ ᆼᄇ ᅥ ᆫᄒ ᅮ ᅬᄀ ᅱᄆ ᅩᄒ ᆼᄋ ᅧ ᆯᄎ ᅳ ᅮᄌ ᆼᄒ ᅥ ᅡᄀ ᅵᄋ ᅱᄒ ᅡᄋ ᅧᄇ ᅦᄋ ᅵᄌ ᅵᄋ ᆫᅡ ᅡ ᆼ ᄇᄇ ᆸᄋ ᅥ ᅳᄅ ᅩ West (2003)ᄀ ᅡᄌ ᅦᄉ ᅵᄒ ᆫᄉ ᅡ ᅡᄌ ᆫᄇ ᅥ ᆫᄑ ᅮ ᅩᄅ ᆯᄉ ᅳ ᅡᄋ ᆼᄒ ᅭ ᆫᄃ ᅡ ᅡ. ᄌᄉ ᅮ ᆼᄇ ᅥ ᆫᄋ ᅮ ᅴᄀ ᆺᄉ ᅢ ᅮ Kᄀ ᅡᄀ ᅩᄌ ᆼᄃ ᅥ ᅬᄋ ᆻᄋ ᅥ ᆯᄄ ᅳ ᅢ, ᄌ ᅮᄉ ᆼᄇ ᅥ ᆫᄒ ᅮ ᅬᄀ ᅱᄆ ᅩᄒ ᆼᄋ ᅧ ᅦᄉ ᅥᄋ ᅴᄆ ᅩᄉ ᅮᄂ ᆫᄋ ᅳ ᆫᄌ ᅵ ᅡᄒ ᅬᄀ ᅱᄆ ᅩᄉ ᅮ (factor regression parameter) θᄋ ᅪᄋ ᅩᄎ ᅡᄇ ᆫᄉ ᅮ ᆫ σ2 ᄋ ᅡ ᅵᄃ ᅡ. θᄋ ᅦᄃ ᅢᄒ ᆫᄉ ᅡ ᅡᄌ ᆫᄇ ᅥ ᆫᄑ ᅮ ᅩᄅ ᅩ West (2003)ᄀ ᅡᄌ ᅦᄉ ᅵᄒ ᆫᄉ ᅡ ᅡᄌ ᆫᄇ ᅥ ᆫᄑ ᅮ ᅩᄂ ᆫᄋ ᅳ ᆯᄇ ᅵ ᆫᄌ ᅡ ᆨᄋ ᅥ ᆫ ᅵ ᆨᄉ ᅮ ᄎ ᅩᅡ ᄉᄌ ᆫᄇ ᅥ ᆫᄑ ᅮ ᅩ (generalized shrinkage prior)ᄅ ᅩᄀ ᆨᄀ ᅡ ᆨᄋ ᅡ ᅴ Kᄀ ᅢᄋ ᅴᄆ ᅩᄉ ᅮᄋ ᅦ t-ᄇ ᆫᄑ ᅮ ᅩᄅ ᆯᄀ ᅳ ᅡᄌ ᆼᄒ ᅥ ᆫᄃ ᅡ ᅡ. t-ᄇ ᆫᄑ ᅮ ᅩᄂ ᆫᄌ ᅳ ᆼ ᅥ ᅲᄇ ᄀ ᆫᅩ ᅮ 퐈 ᄋᄇ ᆫᄉ ᅮ ᆫᄋ ᅡ ᅦᄃ ᅢᄒ ᆫᄀ ᅡ ᆷᄆ ᅡ ᅡᄇ ᆫᄑ ᅮ ᅩᄋ ᅴᄒ ᆫᄒ ᅩ ᆸᄋ ᅡ ᅳᄅ ᅩᄑ ᅭᄒ ᆫᄒ ᅧ ᆯᄉ ᅡ ᅮᄋ ᆻᄂ ᅵ ᆫᄃ ᅳ ᅦ, ᄋ ᅵᄅ ᆯᄒ ᅳ ᆯᄋ ᅪ ᆼᄒ ᅭ ᆫᄎ ᅡ ᆨᄉ ᅮ ᅩᄉ ᅡᄌ ᆫᄇ ᅥ ᆫᄑ ᅮ ᅩᄂ ᆫᄃ ᅳ ᅡᄋ ᆷᄀ ᅳ ᅪᄀ ᇀ ᅡ ᅡ. ᄃ θk ∼ N. 0,. ck ϕk. ,. ϕk ∼ Gamma. r 2. ,. r , 2. (3.1). ᄋᄀ ᅧ ᅵᅦ ᄋᄉ ᅥ rᄋ ᆫᄌ ᅳ ᅩᄋ ᆯᄆ ᅲ ᅩᄉ ᅮ (tuning parameter)ᄅ ᅩᄋ ᆷᄋ ᅵ ᅴᄌ ᆼᄆ ᅥ ᆯᄃ ᅵ ᅩ (random precision) ϕk ᄋ ᅦᄃ ᅢᄒ ᆫᄌ ᅡ ᆨᄇ ᅥ ᆫᄋ ᅮ ᅳᄅ ᅩᄀ ᅮ ᅢᄌ ᄒ ᆫ θk ᄋ ᅵ ᅴᄌ ᅮᄇ ᆫᄇ ᅧ ᆫᄑ ᅮ ᅩ t-ᄇ ᆫᄑ ᅮ ᅩᄋ ᅴᄌ ᅡᄋ ᅲᄃ ᅩᄆ ᅩᄉ ᅮᄋ ᅵᄃ ᅡ. ᄋ ᅧᄀ ᅵᄋ ᅦᄉ ᅥ ck ᄂ ᆫᄌ ᅳ ᅮᄉ ᆼᄇ ᅥ ᆫᄋ ᅮ ᅴᄇ ᆫᄉ ᅮ ᆫᄋ ᅡ ᅵᄏ ᆫᄉ ᅳ ᅮᄅ ᆨᄌ ᅩ ᅮᄉ ᆼᄇ ᅥ ᆫᄒ ᅮ ᅬᄀ ᅱᄆ ᅩ ᆼᄋ ᅧ ᄒ ᅦᄉ ᅥᄋ ᅴᄉ ᆯᅧ ᅥ ᆼ ᄆᄅ ᆨᄋ ᅧ ᅵᄌ ᆨᄋ ᅡ ᅡᄌ ᅵᄃ ᅩᄅ ᆨᄌ ᅩ ᅩᄌ ᆯᄒ ᅥ ᅡᄂ ᆫᄉ ᅳ ᆼᄉ ᅡ ᅮᄅ ᅩᄋ ᅵᄋ ᆫᄀ ᅧ ᅮᄋ ᅦᄉ ᅥᄂ ᆫ ck = ρk−2 ᄋ ᅳ ᅳᄅ ᅩᄉ ᅡᄋ ᆼᄒ ᅭ ᅡᄀ ᅩ ρᄅ ᆯᄎ ᅳ ᅮᄌ ᆼᄒ ᅥ ᆫᄃ ᅡ ᅡ. ᆨ (3.1)ᄋ ᅵ ᄉ ᅴ θᄋ ᅦᄃ ᅢᄒ ᆫᄉ ᅡ ᅡᄌ ᆫᄇ ᅥ ᆫᄑ ᅮ ᅩᄂ ᆫ ᅳ β ∼ M V Np 0, AT GA ᅩᄉ ᄅ ᆼᅡ ᅢ ᆨ 가 ᆯ ᄒᄉ ᅮᄋ ᆻᄃ ᅵ ᅡ. ᄋ ᅧᄀ ᅵᄉ ᅥ G = diag (ck /ϕk )k=1,...,K ᄋ ᅵᄃ ᅡ. ᄃ ᅡᄅ ᆫᄆ ᅳ ᅩᄉ ᅮᄃ ᆯᄋ ᅳ ᅴᄉ ᅡᄌ ᆫᄇ ᅥ ᆫᄑ ᅮ ᅩᄂ ᆫᄃ ᅳ ᅡᄋ ᆷᄀ ᅳ ᅪᄀ ᇀᄃ ᅡ ᅡ. σ 2 ∼ IG (a, b) ,. ρ ∼ π (ρ) ∝ 1,. r ∼ U (0, 10) .. 3.2. 사후표본 추출과정 ᅱᄋ ᄋ ᅦᄉ ᅥᄌ ᅦᄉ ᅵᄒ ᆫᄉ ᅡ ᅡᄌ ᆫᄇ ᅥ ᆫᄑ ᅮ ᅩᅪ ᄋᄋ ᅩᄎ ᅡᄒ ᆼᄋ ᅡ ᅴᄌ ᆼᄀ ᅥ ᅲᄉ ᆼᄀ ᅥ ᅡᄌ ᆼᄋ ᅥ ᅳᄅ ᅩᄇ ᅮᄐ ᅥᄋ ᆮᄋ ᅥ ᆫᄀ ᅳ ᆯᄒ ᅧ ᆸᄉ ᅡ ᅡᄒ ᅮᄇ ᆫᄑ ᅮ ᅩᄂ ᆫᄃ ᅳ ᅡᄋ ᆷᄀ ᅳ ᅪᄀ ᇀᄃ ᅡ ᅡ.. π θ, ϕ, r, ρ, σ 2 |X, y ! K K Y 1 1 X 2 1/2 2 −n/2 T −K/2 2 ∝ σ exp − 2 (y − F θ) (y − F θ) ρ ϕk exp − k ϕk θk 2σ 2ρ k=1 k=1 ! r r2 K Y K K r −1 b rX 2 −(a+1) 2 2 × σ (ϕk ) ϕk . (3.2) exp − 2 exp − K σ 2 Γ r2 k=1 k=1 ᆨ (3.2)ᄅ ᅵ ᄉ ᅩᄇ ᅮᄐ ᅥᄋ ᅴᄆ ᅡᄅ ᅳᄏ ᅩᄑ ᅳᄎ ᅦᄋ ᆫᄆ ᅵ ᆫᄐ ᅩ ᅦᄏ ᅡᄅ ᆯᄅ ᅳ ᅩᅪ ᄀᄌ ᆼᄋ ᅥ ᆫᄃ ᅳ ᅡᄋ ᆷᄀ ᅳ ᅪᄀ ᇀᄃ ᅡ ᅡ. P1. θᄋ ᅴᄌ ᅩᄀ ᆫᄇ ᅥ ᅮᄉ ᅡᄒ ᅮᄇ ᆫᄑ ᅮ ᅩᄅ ᅩᄇ ᅮᄐ ᅥᄑ ᅭᄇ ᆫᅳ ᅩ ᆯ ᄋᄇ ᆯᄉ ᅡ ᆼᄉ ᅢ ᅵᄏ ᆫᄃ ᅵ ᅡ.. . 2. θ|ϕ, r, ρ, σ , X, y ∼ M V NK ᅧᄀ ᄋ ᅵᄋ ᅦᄉ ᅥ D = diag d2k ᅡ. ᄃ. k=1,...,K. 1 2 D + G∗−1 σ2. −1 . −1 ! 1 T 1 2 ∗−1 F y , D +G . σ2 σ2. , dk ᄂ ᆫ Xᄋ ᅳ ᅴ kᄇ ᆫᄍ ᅥ ᅢᄋ ᆼᄋ ᅣ ᅴᄐ ᆨᄋ ᅳ ᅵᄀ ᆹᄋ ᅡ ᅵᄀ ᅩ, G∗ = diag. . ρ k 2 ϕk. k=1,...,K. ᅵ ᄋ.
(6) 252. Minjung Kyung. P2. ϕ = (ϕ1 , · · · , ϕK )T ᄋ ᅴᄌ ᅩᄀ ᆫᄇ ᅥ ᅮᄉ ᅡᄒ ᅮᄇ ᆫᄑ ᅮ ᅩᄅ ᅩᄇ ᅮᄐ ᅥᄑ ᅭᄇ ᆫᅳ ᅩ ᆯ ᄋᄇ ᆯᄉ ᅡ ᆼᄉ ᅢ ᅵᄏ ᆫᄃ ᅵ ᅡ. k = 1, . . . , Kᄋ ᅦᄃ ᅢᄒ ᅡᄋ ᅧ, . 2. ϕk |θ, r, ρ, σ , X, y ∼ Gamma. 1 r 1 2 2 r + , k θk + 2 2 2ρ 2. .. P3. rᄋ ᅦᄃ ᅢᄒ ᅡᄋ ᅧᄌ ᅩᄀ ᆫᄇ ᅥ ᅮᄉ ᅡᄒ ᅮᄇ ᆫᄑ ᅮ ᅩᄅ ᅩᄇ ᅮᄐ ᅥᄑ ᅭᄇ ᆫᄇ ᅩ ᆯᄉ ᅡ ᆼᄋ ᅢ ᆯᄋ ᅳ ᅱᄒ ᅢᄆ ᅦᄐ ᅳᄅ ᅩᄑ ᆯᄅ ᅩ ᅵᄉ ᅳ-ᄒ ᅢᄉ ᅳᄐ ᆼᄉ ᅵ ᅳᄋ ᆯᄀ ᅡ ᅩᄅ ᅵᄌ ᆷ (Metroplisᅳ Hastings algorithm)ᄋ ᆯᄉ ᅳ ᅡᄋ ᆼᄒ ᅭ ᆫᄃ ᅡ ᅡ. rᄋ ᅴᄌ ᅩᄀ ᆫᄇ ᅥ ᅮᄉ ᅡᄒ ᅮᄇ ᆫᄑ ᅮ ᅩᄂ ᆫᄃ ᅳ ᅡᄋ ᆷᄀ ᅳ ᅪᄀ ᇀᄃ ᅡ ᅡ. 2. r 2. . π r|ϕk , θ, ρ, σ , X, y ∝ . Γ. rK. (. 2. r 2. PK. K exp −r. k=1. ϕk. 2. K 1X − ln ϕk 2. !) .. k=1. ᅱ ᄋ ᅴ ᄌ ᄋ ᅩᄀ ᆫᄇ ᅥ ᅮ ᄉ ᅡᄒ ᅮᄇ ᆫᄑ ᅮ ᅩᄅ ᅩ ᄇ ᅮᄐ ᅥ ᄑ ᅭᄇ ᆫ ᄇ ᅩ ᆯᄉ ᅡ ᆼᄋ ᅢ ᆯ ᄋ ᅳ ᅱᅡ ᆫ ᄒ ᄒ ᅮᄇ ᅩᄇ ᆫᄑ ᅮ ᅩ (candidate distribution)ᄂ ᆫ ᄆ ᅳ ᅩᄉ ᅮᄀ ᅡ PK PK 1 k=1 ϕk ᆫᄌ ᅵ ᅵᄉ ᅮᄇ ᆫᄑ ᅮ ᅩᄅ ᅩ − 2 k=1 ln ϕk ᄋ 2 PK. ∗. g (r) = exponential r|. k=1. ϕk. 2. K 1X − ln ϕk 2. !. k=1. ᅵᄃ ᄋ ᅡ. ᄒ ᅮᄇ ᅩᄇ ᆫᄑ ᅮ ᅩᄅ ᆯᄋ ᅳ ᅵᄋ ᆼᄒ ᅭ ᆫᄆ ᅡ ᅦᄐ ᅳᄅ ᅩᄑ ᆯᄅ ᅩ ᅵᄉ ᅳ-ᄒ ᅢᄉ ᅳᄐ ᆼᄉ ᅵ ᅳᅪ ᄀᄌ ᆼᄋ ᅥ ᆫ ᅳ – ᄒ ᅮᄇ ᅩᄇ ᆫᄑ ᅮ ᅩᄅ ᅩᄇ ᅮᄐ ᅥ r′ ᄋ ᆯᄇ ᅳ ᆯᄉ ᅡ ᆼᄉ ᅢ ᅵᄏ ᆫᄃ ᅵ ᅡ: r′ ∼ g ∗ (r) – u ∼ U (0, 1) – ᅡ ᄆᄋ ᆫ ᆨ u ≤ A (r, rt )ᄋ ᅣ ᅵᄆ ᆫ, ᄎ ᅧ ᆺᄃ ᅥ ᆫᄀ ᅡ ᅨᄋ ᅦᄉ ᅥᄇ ᆯᄉ ᅡ ᆼᄉ ᅢ ᅵᄏ ᆫ r′ ᄋ ᅵ ᆯᄉ ᅳ ᅢᄅ ᅩᄋ ᆫ rᄋ ᅮ ᅴᄀ ᆹᄋ ᅡ ᅳᄅ ᅩᄉ ᅡᄋ ᆼᄒ ᅭ ᆫᄃ ᅡ ᅡ, rt+1 = ′ r. ᄋ ᅧᄀ ᅵᄉ ᅥᄎ ᅢᄐ ᆨᄇ ᅢ ᅵᄋ ᆯ (acceptance ration) A (r, rt )ᄂ ᅲ ᆫᄃ ᅳ ᅡᄋ ᆷᄀ ᅳ ᅪᄀ ᇀᄃ ᅡ ᅡ. ! π r′ |ϕk , θ, ρ, σ 2 , X, y g ∗ (rt ) A (r, rt ) = min 1, π (rt |ϕk , θ, ρ, σ 2 , X, y) g ∗ (r′ ) P4. ρᄋ ᅴᄌ ᅩᄀ ᆫᄇ ᅥ ᅮᄉ ᅡᄒ ᅮᄇ ᆫᄑ ᅮ ᅩᄅ ᅩᄇ ᅮᄐ ᅥᄑ ᅭᄇ ᆫᅳ ᅩ ᆯ ᄋᄇ ᆯᄉ ᅡ ᆼᄉ ᅢ ᅵᄏ ᆫᄃ ᅵ ᅡ. 2. ρ|θ, ϕ, r, σ , X, y ∼ IG. K 1X 2 K − 1, k ϕk θk2 2 2. ! .. k=1. P5. σ 2 ᄋ ᅴᄌ ᅩᄀ ᆫᄇ ᅥ ᅮᄉ ᅡᄒ ᅮᄇ ᆫᄑ ᅮ ᅩᄅ ᅩᄇ ᅮᄐ ᅥᄑ ᅭᄇ ᆫᅳ ᅩ ᆯ ᄋᄇ ᆯᄉ ᅡ ᆼᄉ ᅢ ᅵᄏ ᆫᄃ ᅵ ᅡ. σ 2 |θ, ϕ, r, ρ, X, y ∼ IG. . n 1 + a, (y − F θ)T (y − F θ) + b . 2 2. 3.3. 주성분의 갯수 추정과정 ᄇᄋ ᅦ ᅵᄌ ᅵᄋ ᆫᄎ ᅡ ᅮᄅ ᆫᄋ ᅩ ᅦᄉ ᅥᄇ ᅦᄋ ᅵᄌ ᅳᄋ ᆫᄌ ᅵ ᅡᄂ ᆫᄃ ᅳ ᅮᄆ ᅩᄒ ᆼᄋ ᅧ ᆯᄇ ᅳ ᅵᄀ ᅭᄒ ᆯᄄ ᅡ ᅢᄉ ᅡᄒ ᅮᄇ ᆫᄑ ᅮ ᅩᄅ ᆯᄒ ᅳ ᆯᄋ ᅪ ᆼᄒ ᅭ ᅡᄂ ᆫᄇ ᅳ ᆼᄇ ᅡ ᆸᄋ ᅥ ᅵᄃ ᅡ. ᄃ ᅮᄆ ᅩᄒ ᆼ M0 ᄋ ᅧ ᅪ M1 ᄋ ᅦᅢ ᄃᄒ ᅡᄋ ᅧ, M1 ᄃ ᅢᄇ ᅵ M0 ᄅ ᆯᄉ ᅳ ᆫᄒ ᅥ ᅩᄒ ᆯᄉ ᅡ ᅡᄒ ᅮᄋ ᅩᄌ ᅳ (posterior odds)ᄂ ᆫ ᅳ π (M0 |y) π (y|M0 ) π(M0 ) = × π (M0 |y) π (y|M1 ) π(M1 ) ᄋᄅ ᅳ ᅩ π(M )ᄋ ᆫᄆ ᅳ ᅩᄒ ᆼᄋ ᅧ ᅦᄃ ᅢᄒ ᆫᄉ ᅡ ᅡᄌ ᆫᄒ ᅥ ᆨᄅ ᅪ ᆯᄋ ᅲ ᅵᄀ ᅩ, π (y|M )ᄋ ᆫᄆ ᅳ ᅩᄒ ᆼ Mᄋ ᅧ ᆯᄀ ᅳ ᅵᄇ ᆫᄋ ᅡ ᅳᄅ ᅩᄆ ᅩᄉ ᅮᄀ ᆼᄀ ᅩ ᆫᄋ ᅡ ᅦᄃ ᅢᄒ ᆫᄋ ᅡ ᆯᄇ ᅵ ᆫᄌ ᅡ ᆨᄋ ᅥ ᆫᄋ ᅵ ᅮ π(y|M0 ) ᅩᄒ ᄃ ᆷᅮ ᅡ ᄉᄋ ᅴᄑ ᆼᄀ ᅧ ᆫᄋ ᅲ ᅵᄃ ᅡ. ᄇ ᅦᄋ ᅵᄌ ᅳᄋ ᆫᄌ ᅵ ᅡᄂ ᆫ π(y|M1 ) ᄋ ᅳ ᅳᄅ ᅩ, ᄆ ᅩᄉ ᅮᄌ ᆫᄎ ᅥ ᅦᄋ ᅴᄉ ᅡᄌ ᆫᄇ ᅥ ᆫᄑ ᅮ ᅩᄀ ᅡᄃ ᆫᄋ ᅡ ᅱᄌ ᆼᄇ ᅥ ᅩᄋ ᆯᄄ ᅵ ᅢ BICᄂ ᆫᄇ ᅳ ᅦᄋ ᅵᄌ ᅳ ᆫᄌ ᅵ ᄋ ᅡᄅ ᅩᄀ ᆫᄉ ᅳ ᅡᄒ ᆫᄃ ᅡ ᅡᄂ ᆫᄐ ᅳ ᆨᄌ ᅳ ᆼᄋ ᅵ ᅵᄋ ᆻᄃ ᅵ ᅡ (Weakliem, 1999; Raftery, 1999). ᄉ ᆫᅧ ᅥ ᆼ ᄒᄆ ᅩᄒ ᆼᄋ ᅧ ᅴᄀ ᆼᄋ ᅧ ᅮᄇ ᆫᄉ ᅮ ᆫᄋ ᅡ ᅵᄏ ᆫᄉ ᅳ ᅡᄌ ᆫᄇ ᅥ ᆫ ᅮ.
(7) Bayesian PCA regression. 253. ᄑᄅ ᅩ ᆯᄉ ᅳ ᅡᄋ ᆼᄒ ᅭ ᅡᄋ ᅧᄉ ᅡᄌ ᆫᄇ ᅥ ᆫᄑ ᅮ ᅩᄀ ᅡᄃ ᆫᄋ ᅡ ᅱᄌ ᆼᄇ ᅥ ᅩᄋ ᅪᄇ ᅵᄉ ᆺᄒ ᅳ ᆫᅧ ᅡ ᆼ ᄒᄐ ᅢᄅ ᆯᄀ ᅳ ᆽᄃ ᅡ ᅩᄅ ᆨᄉ ᅩ ᅡᄋ ᆼᄒ ᅭ ᅡᄂ ᆫᄇ ᅳ ᆼᄇ ᅡ ᆸᄃ ᅥ ᅩᄋ ᆻᄌ ᅵ ᅵᄆ ᆫ, ᄉ ᅡ ᅡᄌ ᆫᅥ ᅥ ᆼ ᄌᄇ ᅩᄀ ᅡᄌ ᆫᄌ ᅩ ᅢ ᅡᄂ ᄒ ᆫᄀ ᅳ ᆼᄋ ᅧ ᅮ BICᄋ ᅴᄇ ᅦᄋ ᅵᄌ ᅳᄋ ᆫᄌ ᅵ ᅡᄅ ᅩᄋ ᅴᄀ ᆫᄉ ᅳ ᅡᄂ ᆫᄒ ᅳ ᅭᅪ ᄀᄌ ᆨᄋ ᅥ ᅵᄌ ᅵᄋ ᆭᄃ ᅡ ᅡ. ᄀ ᅳᄅ ᅥᄂ ᅡᄇ ᅦᄋ ᅵᄌ ᅳᄋ ᆫᄌ ᅵ ᅡᄋ ᅴᄀ ᆼᄋ ᅧ ᅮᄇ ᅦᄋ ᅵᄌ ᅵᄋ ᆫᄆ ᅡ ᅩᄉ ᅮ ᅮᄌ ᄎ ᆼᅩ ᅥ ᄇᄃ ᅡᄉ ᅡᄌ ᆫᅥ ᅥ ᆼ ᄌᄇ ᅩᄋ ᅦᄆ ᅢᄋ ᅮᄆ ᆫᄀ ᅵ ᆷᄒ ᅡ ᅡᄃ ᅡ (Raftery, 1999). ᄀ ᅦᄃ ᅡᄀ ᅡᄇ ᅦᄋ ᅵᄌ ᅳᄋ ᆫᄌ ᅵ ᅡᄅ ᆯᄀ ᅳ ᅨᄉ ᆫᄒ ᅡ ᅡᄀ ᅵᄋ ᅱᅡ ᆫ ᄒᄆ ᅩᄉ ᅮᄀ ᆼᄀ ᅩ ᆫᄋ ᅡ ᅦ ᅢᄒ ᄃ ᆫᆯ ᅡ ᅵᄇ ᄋ ᆫᄌ ᅡ ᆨᄋ ᅥ ᆫᄋ ᅵ ᅮᄃ ᅩᄒ ᆷᄉ ᅡ ᅮᄋ ᅴᄑ ᆼᄀ ᅧ ᆫ π (y|M )ᄋ ᅲ ᅴᄀ ᅨᄉ ᆫᄋ ᅡ ᅵᄆ ᅢᄋ ᅮᄋ ᅥᄅ ᆸᄃ ᅧ ᅡᄂ ᆫᄃ ᅳ ᆫᄌ ᅡ ᆷᄋ ᅥ ᅵᄋ ᆻᄃ ᅵ ᅡ. Z π (y|θ, M ) π (θ, |M ) dθ. π (y|M ) = θ. ᄋᄀ ᅴ ᅨᄉ ᆫᄋ ᅡ ᅦᄉ ᅥᄐ ᆨᄒ ᅳ ᅵ θᄀ ᅡᄀ ᅩᄎ ᅡᄋ ᆫ (high demensions)ᄋ ᅯ ᆫᅧ ᅵ ᆼ ᄀᄋ ᅮᄑ ᅨᄉ ᅫᄒ ᆼ (closed-form)ᄋ ᅧ ᅴᄀ ᅨᄉ ᆫᄋ ᅡ ᆫᅮ ᅳ ᆯ ᄇᄀ ᅡᄂ ᆼᄒ ᅳ ᅡᄃ ᅡ (Kass and Raftery, 1995). ᄋ ᅵᄅ ᅥᄒ ᆫᄇ ᅡ ᅦᄋ ᅵᄌ ᅳᄋ ᆫᄌ ᅵ ᅡᄋ ᅴᄃ ᆫᄌ ᅡ ᆷᄃ ᅥ ᅢᄉ ᆫᄋ ᅵ ᆯᄇ ᅵ ᆫᄌ ᅡ ᆨᄋ ᅥ ᅳᄅ ᅩᄆ ᅩᄒ ᆼᄉ ᅧ ᆫᅢ ᅥ ᆨ ᄐᄋ ᅦᄉ ᅥᄃ ᆫᄋ ᅡ ᅱᄌ ᆼᄇ ᅥ ᅩᄋ ᅴᄉ ᅡᄌ ᆫ ᅥ ᆫᄑ ᅮ ᄇ ᅩᄀ ᅡᄋ ᅡᄂ ᆫᄋ ᅵ ᆯᄇ ᅵ ᆫᄌ ᅡ ᆨᄋ ᅥ ᆫᄉ ᅵ ᅡᄌ ᆫᄇ ᅥ ᆫᄑ ᅮ ᅩᄅ ᆯᄀ ᅳ ᅩᄅ ᅧᄒ ᆫ BICᄅ ᅡ ᆯᄉ ᅳ ᅡᄋ ᆼᄒ ᅭ ᅡᄂ ᆫᄀ ᅳ ᆺᄋ ᅥ ᅵᄃ ᅥᄌ ᇂᄋ ᅩ ᆫᄀ ᅳ ᅵᄌ ᆫᄋ ᅮ ᅵᄃ ᆯᄉ ᅬ ᅮᄋ ᆻᄃ ᅵ ᅡ. BICᄋ ᅦᄃ ᅢ ᆫᄃ ᅡ ᄒ ᆫᄌ ᅡ ᆷᄃ ᅥ ᆯᄃ ᅳ ᅩᄌ ᆫᄌ ᅩ ᅢᄒ ᅡᄌ ᅵᄆ ᆫ, ᄋ ᅡ ᅵᄋ ᆫᄀ ᅧ ᅮᄋ ᅦᄉ ᅥᄀ ᅩᄅ ᅧᄒ ᅡᄂ ᆫᄆ ᅳ ᅩᄒ ᆼᄋ ᅧ ᆫᄉ ᅳ ᆫᅧ ᅥ ᆼ ᄒᄆ ᅩᄒ ᆼᄋ ᅧ ᅳᄅ ᅩᄉ ᅡᄌ ᆫᅥ ᅥ ᆼ ᄌᄇ ᅩᄋ ᅦᄆ ᆫᄀ ᅵ ᆷᄒ ᅡ ᅡᄀ ᅩᄇ ᆨᄌ ᅩ ᆸᅡ ᅡ ᆫ ᄒᄀ ᅨᄉ ᆫ ᅡ ᆨᄋ ᅵ ᄉ ᆯᅡ ᅳ ᄉᄋ ᆼᄒ ᅭ ᅡᄂ ᆫᄇ ᅳ ᅦᄋ ᅵᄌ ᅳᄋ ᆫᄌ ᅵ ᅡᄇ ᅩᄃ ᅡᄂ ᆫ BICᄅ ᅳ ᆯᄆ ᅳ ᅩᄒ ᆼᅥ ᅧ ᆫ 새 ᆨ ᄐᄋ ᅦᄉ ᅡᄋ ᆼᄒ ᅭ ᆫᄃ ᅡ ᅡ. BICᄂ ᆫ ᅳ BIC = −2 × ln (b π ) + p ln(n) ᄋᄅ ᅳ ᅩπ bᄂ ᆫᄇ ᅳ ᅦᄋ ᅵᄌ ᅵᄋ ᆫᄉ ᅡ ᅡᄒ ᅮᅬ ᄎᄇ ᆫᄀ ᅵ ᆹ (Bayesian posterior mode)ᄋ ᅡ ᅵᄀ ᅩ pᄂ ᆫᄆ ᅳ ᅩᄉ ᅮᄋ ᅴᄀ ᆺᄉ ᅢ ᅮᄋ ᅵᄃ ᅡ. ᅵᄋ ᄋ ᆫᄀ ᅧ ᅮᄋ ᅦᄉ ᅥᄂ ᆫ BICᄅ ᅳ ᆯᄀ ᅳ ᅩᄅ ᅧᄒ ᅡᄂ ᆫᄋ ᅳ ᅵᄋ ᅲᄂ ᆫᄌ ᅳ ᅮᄉ ᆼᄇ ᅥ ᆫᄋ ᅮ ᅴᄀ ᆺᄉ ᅢ ᅮᄋ ᆫ Kᄅ ᅵ ᆯᄀ ᅳ ᆯᅥ ᅧ ᆼ ᄌᄒ ᅡᄂ ᆫᄃ ᅳ ᅦᄋ ᆻᄋ ᅵ ᅥᄉ ᅥᄌ ᆨᅥ ᅥ ᆯ ᄌᄒ ᆫᄀ ᅡ ᅵᄌ ᆫᅳ ᅮ ᆯ ᄋᄌ ᅦᄉ ᅵ ᅡᄀ ᄒ ᅵᅱ 아 ᆷ ᄒᄋ ᅵᄃ ᅡ. ᄌ ᅮᄉ ᆼᄇ ᅥ ᆫᄉ ᅮ ᆫᅧ ᅥ ᆼ ᄒᄆ ᅩᄒ ᆼᄋ ᅧ ᅦᄉ ᅥᄇ ᅵᄀ ᅭᄒ ᅡᄂ ᆫᄆ ᅳ ᅩᄒ ᆼᄃ ᅧ ᆯᄋ ᅳ ᆫᄌ ᅳ ᅮᄉ ᆼᄇ ᅥ ᆫᄋ ᅮ ᅴᄀ ᆺᄉ ᅢ ᅮᄅ ᆯᄉ ᅳ ᆫᅢ ᅥ ᆨ ᄐᄒ ᅡᄂ ᆫᅮ ᅳ ᆫ ᄆᄌ ᅦᄋ ᅵᄆ ᅳᄅ ᅩᄃ ᆫᄉ ᅡ ᆫᄒ ᅮ ᅵ ᆯᅧ ᅥ ᄉ ᆼ 며 ᆫ ᄇᄉ ᅮᄃ ᆯᄋ ᅳ ᅴᄀ ᆫᄀ ᅪ ᅨᄉ ᆨᄋ ᅵ ᅳᄅ ᅩᄆ ᆫᄃ ᅡ ᆯᄋ ᅳ ᅥᄌ ᅵᄂ ᆫᄌ ᅳ ᅮᄉ ᆼᄇ ᅥ ᆫᄋ ᅮ ᅴᄀ ᆺᄉ ᅢ ᅮᄅ ᆯᄉ ᅳ ᆫᄐ ᅥ ᆨᄒ ᅢ ᅡᄂ ᆫᅮ ᅳ ᆫ ᄆᄌ ᅦᄀ ᅡᄋ ᅡᄂ ᆫᄇ ᅵ ᆫᄋ ᅡ ᆼᄇ ᅳ ᆫᄉ ᅧ ᅮᅪ ᄋᄋ ᅴᄉ ᆯᅧ ᅥ ᆼ ᄆᄅ ᆨᄋ ᅧ ᅵᄂ ᇁ ᅩ ᆫᄇ ᅳ ᄋ ᅮᄇ ᆫᄌ ᅮ ᆸᄒ ᅵ ᆸᄌ ᅡ ᅮᄉ ᆼᄇ ᅥ ᆫᅳ ᅮ ᆯ ᄋᄉ ᆫᄐ ᅥ ᆨᄒ ᅢ ᅡᄀ ᅵᄋ ᅱᄒ ᅡᄋ ᅧ BICᄅ ᆯᄉ ᅳ ᅡᄋ ᆼᄒ ᅭ ᆫᄃ ᅡ ᅡ.. 4. 자료분석 ᆨᅡ ᅡ ᄀ ᄌᄅ ᅭᄋ ᅦᄃ ᅢᄒ ᅡᄋ ᅧ, 3ᄌ ᆼᄋ ᅡ ᅦᄉ ᅥᄉ ᆯᄆ ᅥ ᆼᄒ ᅧ ᆫᄉ ᅡ ᅡᄌ ᆫᄇ ᅥ ᆫᄑ ᅮ ᅩᄅ ᆯᄀ ᅳ ᅩᄅ ᅧᄒ ᆫ MCMC ᄀ ᅡ ᅪᄌ ᆼᄋ ᅥ ᆯ K = 2, . . . , min(n, p)ᄋ ᅳ ᅴᄇ ᆷᄋ ᅥ ᅱ ᄋᄆ ᅦ ᅩᅮ ᄃᄌ ᆨᄋ ᅥ ᆼᄒ ᅭ ᆫᄒ ᅡ ᅮᄀ ᆨᄀ ᅡ ᆨᄋ ᅡ ᅴ Kᄀ ᆹᄋ ᅡ ᅦᄃ ᅢᄒ ᆫ BICᄅ ᅡ ᆯᄀ ᅳ ᅮᄒ ᅡᄋ ᅧ BICᄀ ᆹᄋ ᅡ ᅵᄀ ᅡᄌ ᆼᄌ ᅡ ᆨᄋ ᅡ ᆫ Kᄅ ᅳ ᆯᄎ ᅳ ᅬᄌ ᆼᄆ ᅩ ᅩᄒ ᆼᄋ ᅧ ᅴᄌ ᅮᄉ ᆼᄇ ᅥ ᆫᄋ ᅮ ᅴ ᆺᄉ ᅢ ᄀ ᅮᅳ ᆨ ᄌᄉ ᅥᄅ ᅩᄌ ᆨᄀ ᅵ ᅭᄋ ᆫᄒ ᅵ ᅬᄀ ᅱᄇ ᆫᄉ ᅧ ᅮᄋ ᅴᄀ ᆺᄉ ᅢ ᅮᄅ ᅩᄀ ᆯᄌ ᅧ ᆼᄒ ᅥ ᅡᄋ ᅧᄆ ᅩᄉ ᅮᄅ ᆯᄎ ᅳ ᅮᄌ ᆼᄒ ᅥ ᅡᄀ ᅩ, ᄃ ᅡᄉ ᅵᄀ ᆨᄉ ᅡ ᆯᅧ ᅥ ᆼ ᄆᄇ ᆫᄉ ᅧ ᅮᄋ ᅴᄆ ᅩᄉ ᅮᄅ ᅩᄋ ᆨᅧ ᅧ ᆫ ᄇᄒ ᆫ ᅪ ᆫᄃ ᅡ ᄒ ᅡ. ᄑ ᅭᄇ ᆫᄀ ᅩ ᅪᄌ ᆼᄋ ᅥ ᆫᄀ ᅳ ᆨᄀ ᅡ ᆨᄋ ᅡ ᅴ Kᄀ ᆹᄋ ᅡ ᅦᄃ ᅢᄒ ᅡᄋ ᅧ 30, 000ᄇ ᆫᄇ ᅥ ᆫᄇ ᅡ ᆨᄒ ᅩ ᅡᄋ ᅧ 15, 000ᄋ ᆫ burn-inᄋ ᅳ ᅳᄅ ᅩᄌ ᅦᄀ ᅥᄒ ᆫᄒ ᅡ ᅮᄂ ᅡᄆ ᅥᄌ ᅵ 15, 000ᄀ ᅢᄋ ᅴᄑ ᅭᄇ ᆫᅮ ᅩ ᆼ ᄌᄆ ᅢ 5ᄇ ᆫᄍ ᅥ ᅢᄑ ᅭᄇ ᆫᄆ ᅩ ᆫᄋ ᅡ ᆯᄉ ᅳ ᆫᅢ ᅥ ᆨ ᄐᄒ ᅡᄋ ᅧᅬ ᄎᄌ ᆼ 3, 000ᄀ ᅩ ᅢᄋ ᅴᄑ ᅭᄇ ᆫᅳ ᅩ ᆯ ᄋᄉ ᅡᄒ ᅮᄎ ᅮᄅ ᆫᄋ ᅩ ᅦᄉ ᅡᄋ ᆼᄒ ᅭ ᆫᄃ ᅡ ᅡ. 4.1. 쿠키반죽자료 ᅵᄌ ᄋ ᅡᄅ ᅭᄂ ᆫ Osborne ᄃ ᅳ ᆼ (1984)ᄋ ᅳ ᅴᄇ ᅵᄉ ᅳᄏ ᆺᄇ ᅵ ᆫᄌ ᅡ ᆨᄌ ᅮ ᅩᄀ ᆨ (ᄒ ᅡ ᆼᅥ ᅧ ᆼ ᄉᄃ ᅬᄋ ᆻᄋ ᅥ ᅳᄂ ᅡᄀ ᆸᄌ ᅮ ᅵᄋ ᆭᄋ ᅡ ᆫᄇ ᅳ ᅵᄉ ᅳᄏ ᆺ)ᄋ ᅵ ᅴᄌ ᅩᄉ ᆼ (comᅥ positiona)ᄎ ᆨᄌ ᅳ ᆼᄋ ᅥ ᅦ NIR ᄇ ᆫᄀ ᅮ ᆼᄀ ᅪ ᅵ ᄉ ᅡᄋ ᆼ ᄀ ᅭ ᅡᄂ ᆼᄉ ᅳ ᆼᄋ ᅥ ᆯ ᄀ ᅳ ᆷᅥ ᅥ ᆼ ᄌᄒ ᅡᄀ ᅵ ᄋ ᅱᄒ ᅢ ᄉ ᅮᄒ ᆼ ᄃ ᅢ ᆫ ᄉ ᅬ ᆯᄒ ᅵ ᆷᄋ ᅥ ᅦᄉ ᅥ ᄇ ᆯᄉ ᅡ ᆼᄒ ᅢ ᆫ ᄋ ᅡ ᆼᄌ ᅣ ᆨᄌ ᅥ ᅡᄅ ᅭᄋ ᅵᄃ ᅡ. ᆫᄌ ᅯ ᄋ ᅡᅭ ᄅᄂ ᆫ 40ᄀ ᅳ ᅢᄇ ᆫᄌ ᅡ ᆨᄋ ᅮ ᅴᄒ ᆫᄅ ᅮ ᆫᄌ ᅧ ᅡᄅ ᅭᅪ ᄋ 32ᄀ ᅢᄇ ᆫᄌ ᅡ ᆨᄋ ᅮ ᅴᄀ ᆷᄌ ᅥ ᆼᄌ ᅳ ᅡᄅ ᅭᄅ ᅩᄂ ᅡᄂ ᅮᄋ ᅥᄌ ᆫᄌ ᅵ ᅡᄅ ᅭᄅ ᅩ 1100ᄋ ᅦᄉ ᅥ 2400 ᄂ ᅡᄂ ᅩᄆ ᅵᄐ ᅥ (nanometer) ᄉ ᅡᄋ ᅵᄋ ᅴ 700ᄀ ᅢᄋ ᅴᄌ ᆷᄋ ᅥ ᅳᄅ ᅩᄀ ᅮᄉ ᆼᄃ ᅥ ᅬᄋ ᅥᄋ ᆻᄃ ᅵ ᅡ. ᄌ ᅵᄇ ᆼ (fat), ᄌ ᅡ ᅡᄃ ᆼ (sucrose), ᄀ ᅡ ᆫᄌ ᅥ ᅩᄀ ᅡᄅ ᅮ (dry flour) ᅳᄅ ᄀ ᅵᅩ ᄀᄉ ᅮᄇ ᆫᄒ ᅮ ᆷᄅ ᅡ ᆼ (water)ᄋ ᅣ ᅴᄇ ᆨᄇ ᅢ ᆫᄋ ᅮ ᆯᅳ ᅲ ᆯ ᄋᄇ ᆫᄋ ᅡ ᆼᄇ ᅳ ᆫᄉ ᅧ ᅮᄅ ᅩᄎ ᆨᄌ ᅳ ᆼᄒ ᅥ ᅡᄋ ᆻᄃ ᅧ ᅡ. ᄋ ᅮᄅ ᅵᄂ ᆫᄋ ᅳ ᅵᄌ ᅡᄅ ᅭᄋ ᅦᄉ ᅥ p = 700ᄀ ᅢᄋ ᅴᄀ ᆫᄎ ᅪ ᆨᄌ ᅳ ᆷ ᅥ ᅳᄅ ᄋ ᅩᄋ ᅵᄅ ᅮᄋ ᅥᄌ ᆫ 72ᄀ ᅵ ᅢᄋ ᅴᄇ ᅵᄉ ᅳᄏ ᆺᄇ ᅵ ᆫᄌ ᅡ ᆨᄌ ᅮ ᅡᄅ ᅭᄋ ᅦᄃ ᅢᄒ ᅡᄋ ᅧᄌ ᅵᄇ ᆼᅡ ᅡ ᆷ ᄒᄅ ᆼᄋ ᅣ ᆯᄇ ᅳ ᆫᄋ ᅡ ᆼᄇ ᅳ ᆫᄉ ᅧ ᅮᄅ ᅩᄒ ᅡᄂ ᆫᄇ ᅳ ᅦᄋ ᅵᄌ ᅵᄋ ᆫᄌ ᅡ ᅮᄉ ᆼᄇ ᅥ ᆫᄒ ᅮ ᅬᄀ ᅱᄆ ᅩ ᆼᄋ ᅧ ᄒ ᆯᅥ ᅳ ᆨ ᄌᄋ ᆼᄒ ᅭ ᆫᄃ ᅡ ᅡ. ᅡᄉ ᄀ ᅵᄌ ᆨᄋ ᅥ ᆫᄒ ᅵ ᆨᄋ ᅪ ᆫᄋ ᅵ ᆯᄋ ᅳ ᅱᄒ ᅢ p = 700ᄀ ᅢᄋ ᅴᄇ ᆫᄀ ᅮ ᆼᄀ ᅪ ᅵᄀ ᆫᄎ ᅪ ᆨᄌ ᅳ ᆷᄋ ᅥ ᅴ n = 72ᄀ ᅢᄋ ᅴᄌ ᅡᄅ ᅭᄋ ᅦᄃ ᅢᄒ ᆫᄀ ᅡ ᅳᄅ ᆷᄀ ᅵ ᅪᄌ ᅮᄉ ᆼᄇ ᅥ ᆫᅮ ᅮ ᆫ ᄇᄉ ᆨᄋ ᅥ ᆯ ᅳ ᅵᄒ ᄉ ᆼᅡ ᅢ ᄒᄋ ᅧᄀ ᅳᄅ ᆫᄉ ᅵ ᅳᄏ ᅳᄅ ᅵᄀ ᅳᄅ ᆷ (scree plot)ᄋ ᅵ ᆫᄀ ᅳ ᅳᄅ ᆷ 4.1ᄋ ᅵ ᅦᄉ ᅥᄒ ᆨᄋ ᅪ ᆫᄒ ᅵ ᆯᄉ ᅡ ᅮᄋ ᆻᄃ ᅵ ᅡ. ᄏ ᅮᄏ ᅵᄇ ᆫᄌ ᅡ ᆨᄌ ᅮ ᅡᄅ ᅭᄂ ᆫ 700ᄀ ᅳ ᅢᄋ ᅴᄀ ᆫ ᅪ ᆨᄌ ᅳ ᄎ ᆷᅳ ᅥ ᆯ ᄃᄋ ᅴᄀ ᆫᄎ ᅪ ᆨᄀ ᅳ ᆹᄋ ᅡ ᅴᄎ ᅡᄋ ᅵᄆ ᆫᄇ ᅡ ᅩᄋ ᆯᄈ ᅵ ᆫ 0ᄋ ᅮ ᆯᄌ ᅳ ᆼᄉ ᅮ ᆷᄋ ᅵ ᅳᄅ ᅩᄉ ᆼᄒ ᅡ ᅡᄃ ᅢᄎ ᆼᄋ ᅵ ᅴᄑ ᅢᄐ ᆫᄋ ᅥ ᆯᄇ ᅳ ᅩᄋ ᅵᄀ ᅩᄋ ᆻᄃ ᅵ ᅡ. ᄋ ᅵᄌ ᅡᄅ ᅭᄋ ᅴᄌ ᅮᄉ ᆼᄇ ᅥ ᆫᅮ ᅮ ᆫ ᄇ ᆨᄉ ᅥ ᄉ ᅳᄏ ᅳᄅ ᅵᄀ ᅳᄅ ᆷᄋ ᅵ ᆯᄇ ᅳ ᅩᄆ ᆫᄌ ᅧ ᅮᄉ ᆼᄇ ᅥ ᆫ 2ᄀ ᅮ ᅢᄄ ᅩᄂ ᆫ 3ᄀ ᅳ ᅢᄅ ᆯᄉ ᅳ ᅡᄋ ᆼᄒ ᅭ ᅡᄋ ᅧᄃ ᅩᄋ ᅵᄌ ᅡᄅ ᅭᄅ ᆯᄎ ᅳ ᆼᄇ ᅮ ᆫᄒ ᅮ ᅵᄉ ᆯᄆ ᅥ ᆼᄒ ᅧ ᆯᄉ ᅡ ᅮᄋ ᆻᄂ ᅵ ᆫᄀ ᅳ ᆺᄋ ᅥ ᆯᄒ ᅳ ᆨᄋ ᅪ ᆫᄒ ᅵ ᆯ ᅡ ᅮᄋ ᄉ ᆻᄃ ᅵ ᅡ. ᄎ ᆺᅥ ᅥ ᆫ ᄇᄍ ᅢᄌ ᅮᄉ ᆼᄇ ᅥ ᆫᄋ ᅮ ᆫᄌ ᅳ ᆫᄎ ᅥ ᅦᄇ ᆫᄉ ᅮ ᆫᄋ ᅡ ᅴ 88%ᄅ ᆯᄉ ᅳ ᆯᅧ ᅥ ᆼ ᄆᄒ ᆯᅥ ᅡ ᆼ ᄌᄃ ᅩᄅ ᅩᄉ ᆯᅧ ᅥ ᆼ ᄆᄅ ᆨᄋ ᅧ ᅵᄏ ᅳᄆ ᅧ, ᄆ ᅩᄃ ᆫ 700ᄀ ᅳ ᅢᄋ ᅴᄀ ᆫᄎ ᅪ ᆨᄌ ᅳ ᆷᄃ ᅥ ᆯᄋ ᅳ ᅴ ᅡᄌ ᄀ ᆼᄑ ᅮ ᆼᄀ ᅧ ᆫᄋ ᅲ ᅳᄅ ᅩᄉ ᆯᅧ ᅥ ᆼ ᄆᄒ ᆯᄉ ᅡ ᅮᄋ ᆻᄃ ᅵ ᅡ. ᄃ ᅮᄇ ᆫᄍ ᅥ ᅢᄌ ᅮᄉ ᆼᄇ ᅥ ᆫᄋ ᅮ ᆫᄌ ᅳ ᆫᄎ ᅥ ᅦᄇ ᆫᄉ ᅮ ᆫᄋ ᅡ ᅴ 10%ᄅ ᆯᄉ ᅳ ᆯᅧ ᅥ ᆼ ᄆᄒ ᅡᄆ ᅧ, ᄋ ᆼᄋ ᅣ ᅴᄇ ᅮᄒ ᅩᄋ ᅪᄋ ᆷᄋ ᅳ ᅴᄇ ᅮᄒ ᅩ.
(8) 254. 400 300. Variances. 0.1. 0. −0.2. 100. −0.1. 200. 0.0. centered spectrum. 0.2. 500. 600. 0.3. Minjung Kyung. 1200. 1400. 1600. 1800. 2000. 2200. 2400. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. wavelength(nm). 0.04. Figure 4.1 NIR spectroscopy plot and scree plot of biscuit dough data. 0.02. 1500000. 0.00 −0.02. beta. BIC. 1000000. −0.04. 500000. 0. 1200 0. 20. 40. K. 60. 1400. 1600. 1800. 2000. 2200. 2400. wavelength(nm). Figure 4.2 BIC plot of the number of principal components and estimates of wavelength regression coefficients β for biscuit dough data. ᄅᄀ ᅩ ᅮᄇ ᆫᄃ ᅮ ᅬᄂ ᆫᄃ ᅳ ᅮᄀ ᅳᄅ ᆸᄋ ᅮ ᅳᄅ ᅩᄂ ᅡᄂ ᅱᄋ ᅥᄌ ᆫᄃ ᅵ ᅡ. ᄃ ᅮᄇ ᆫᄍ ᅥ ᅢᄌ ᅮᄉ ᆼᄇ ᅥ ᆫᄁ ᅮ ᅡᄌ ᅵᄋ ᅴᄂ ᅮᄌ ᆨᄇ ᅥ ᆫᄉ ᅮ ᆫᄋ ᅡ ᆫᄌ ᅳ ᆫᄎ ᅥ ᅦᄇ ᆫᄉ ᅮ ᆫᄋ ᅡ ᅴ 98%ᄅ ᅩᄌ ᆫᄎ ᅥ ᅦᄇ ᆫᄉ ᅮ ᆫ ᅡ ᅴᄃ ᄋ ᅢᄇ ᅮᄇ ᆫᅳ ᅮ ᆯ ᄋᄉ ᆯᅧ ᅥ ᆼ ᄆᄒ ᆫᄃ ᅡ ᅡᄀ ᅩᄒ ᆯᄉ ᅡ ᅮᄋ ᆻᄃ ᅵ ᅡ. 72ᄀ ᅢᄋ ᅴᄌ ᅮᄉ ᆼᄇ ᅥ ᆫᄋ ᅮ ᅳᄅ ᅩᄀ ᅮᄉ ᆼᄃ ᅥ ᆫᄒ ᅬ ᅬᄀ ᅱᄇ ᆫᄉ ᅧ ᅮᄋ ᅦᄃ ᅢᄒ ᅡᄋ ᅧ K = 2ᄋ ᅦᄉ ᅥ 72ᄁ ᅡᄌ ᅵᄀ ᅩᄅ ᅧᄒ ᅡᄋ ᅧᄀ ᅨᄉ ᆫᅡ ᅡ ᆫ ᄒ BICᄋ ᅴᄀ ᆹᄋ ᅡ ᅵᄀ ᅳ ᆷ 2ᄋ ᅵ ᄅ ᅦᄋ ᆻᄃ ᅵ ᅡ. ᄉ ᅳᄏ ᅳᄅ ᅵᄀ ᅳᄅ ᆷᄋ ᅵ ᅴᄌ ᅮᄉ ᆼᄇ ᅥ ᆫᄀ ᅮ ᅳᄅ ᆷᄀ ᅵ ᅪᄂ ᆫᄃ ᅳ ᆯᄅ ᅡ ᅵ BICᄅ ᆯᄉ ᅳ ᅡᄋ ᆼᄒ ᅭ ᅡᄋ ᅧᄉ ᆫᅢ ᅥ ᆨ ᄐᄒ ᆫᄌ ᅡ ᅮᄉ ᆼᄇ ᅥ ᆫᄒ ᅮ ᅬᄀ ᅱᄋ ᅦᄉ ᅥᄋ ᅴᄒ ᅬᄀ ᅱᄇ ᆫᄉ ᅧ ᅮ ᅴᄀ ᄋ ᆺᄉ ᅢ ᅮᄂ ᆫ 12ᄅ ᅳ ᅩᄂ ᅮᄌ ᆨᄇ ᅥ ᆫᄉ ᅮ ᆫᄋ ᅡ ᅵᄌ ᆫᄎ ᅥ ᅦᄇ ᆫᄉ ᅮ ᆫᄋ ᅡ ᅴ 99.98%ᄅ ᆯᄉ ᅳ ᆯᅧ ᅥ ᆼ ᄆᄒ ᅡᄂ ᆫᄌ ᅳ ᅮᄉ ᆼᄇ ᅥ ᆫᄋ ᅮ ᅵᄃ ᅡ. θᄋ ᅴᄎ ᅮᄌ ᆼᄀ ᅥ ᆹᄋ ᅡ ᆯᄇ ᅳ ᅩᄆ ᆫ K = 2ᄁ ᅧ ᅡ ᅵᄋ ᄌ ᅴᄌ ᅮᄉ ᆼᄇ ᅥ ᆫᄆ ᅮ ᅩᄉ ᅮᄋ ᅴ 95% ᄉ ᆫᄋ ᅵ ᆼᄀ ᅭ ᅮᄀ ᆫ (credible interval)ᄋ ᅡ ᆫ 0ᄋ ᅳ ᆯᄑ ᅳ ᅩᄒ ᆷᄒ ᅡ ᅡᄀ ᅩᄋ ᆻᄌ ᅵ ᅵᄋ ᆭᄌ ᅡ ᅵᄆ ᆫᄂ ᅡ ᅡᄆ ᅥᄌ ᅵᄎ ᅮᄌ ᆼᄀ ᅥ ᆹᄋ ᅡ ᅦᄃ ᅢ ᅢᄉ ᄒ ᅥᄂ ᆫᄉ ᅳ ᆫᄋ ᅵ ᆼᄀ ᅭ ᅮᄀ ᆫᄋ ᅡ ᅦ 0ᄋ ᆯᄑ ᅳ ᅩᄒ ᆷᄒ ᅡ ᅡᄂ ᆫᄒ ᅳ ᆼᄐ ᅧ ᅢᄅ ᆯᄇ ᅳ ᅩᄋ ᆫᄃ ᅵ ᅡ (ᄑ ᅭᄂ ᆫᄉ ᅳ ᆼᄅ ᅢ ᆨᅡ ᅣ ᆫ ᄒᄃ ᅡ). MCMC ᄋ ᆯᄀ ᅡ ᅩᄅ ᅵᄌ ᆷᄋ ᅳ ᅴᄉ ᅮᄅ ᆷᅥ ᅧ ᆼ ᄉᄋ ᆯᄒ ᅳ ᆨᄋ ᅪ ᆫ ᅵ ᅡᄀ ᄒ ᅵᄋ ᅱᅡ ᆫ ᄒᄎ ᅮᄎ ᆨᄀ ᅥ ᅳᄅ ᆷ (traceplot)ᄋ ᅵ ᆫᄇ ᅳ ᅮᄅ ᆨᄋ ᅩ ᅦᄉ ᅥᄒ ᆨᄋ ᅪ ᆫᄒ ᅵ ᆯᄉ ᅡ ᅮᄋ ᆻᄃ ᅵ ᅡ. K = 12ᄀ ᅢᄋ ᅴᄌ ᅮᄉ ᆼᄇ ᅥ ᆫᄋ ᅮ ᆯᄒ ᅳ ᅬᄀ ᅱᄇ ᆫᄉ ᅧ ᅮᄅ ᅩᄉ ᅡᄋ ᆼ ᅭ ᆫᄌ ᅡ ᄒ ᅮᄉ ᆼᄇ ᅥ ᆫᄒ ᅮ ᅬᄀ ᅱᄆ ᅩᄒ ᆼᄋ ᅧ ᅦᄉ ᅥᄃ ᅡᄉ ᅵᄋ ᆫᄅ ᅯ ᅢᄋ ᅴᄉ ᆯᅧ ᅥ ᆼ 며 ᆫ ᄇᄉ ᅮᄋ ᅴᄆ ᅩᄉ ᅮ βᄅ ᅩᄋ ᆨᄇ ᅧ ᆫᄒ ᅧ ᆫᄒ ᅪ ᆫ β = AT θᄋ ᅡ ᅴᄎ ᅮᄌ ᆼᄀ ᅥ ᆹᄋ ᅡ ᆯᄉ ᅳ ᆯᄑ ᅡ ᅧᄇ ᅩᄆ ᆫ ᅧ 1720nm ᄀ ᆫᄎ ᅳ ᅥᄋ ᅦᄌ ᆼᄉ ᅥ ᆼ (peak)ᄋ ᅡ ᆯᄀ ᅳ ᇀᄂ ᅡ ᆫᄏ ᅳ ᆯᄅ ᅳ ᅥᄉ ᅳᄐ ᅥᄀ ᅡᄒ ᆼᅥ ᅧ ᆼ ᄉᄃ ᆷᄋ ᅬ ᆯᄒ ᅳ ᆨᄋ ᅪ ᆫᄒ ᅵ ᆯᄉ ᅡ ᅮᄋ ᆻᄃ ᅵ ᅡ. Brown (1999)ᄋ ᅵᄇ ᆫᄉ ᅮ ᆨᄒ ᅥ ᆫᄌ ᅡ ᅡ ᅭᄋ ᄅ ᅦᅥ ᄉ 1718nm ᄋ ᅦᄉ ᅥᄌ ᅵᄇ ᆼᄋ ᅡ ᅴᄐ ᆨᄌ ᅳ ᆼᄌ ᅵ ᆨᄋ ᅥ ᆫᄒ ᅵ ᆸᄀ ᅳ ᆼᄃ ᅪ ᅩᄅ ᆯᄉ ᅳ ᆯᅧ ᅥ ᆼ ᄆᄒ ᅡᄋ ᆻᄂ ᅧ ᆫᄃ ᅳ ᅦ, ᄋ ᅵᄋ ᆨᄉ ᅧ ᅵᄇ ᅦᄋ ᅵᄌ ᅵᄋ ᆫᄌ ᅡ ᅮᄉ ᆼᄇ ᅥ ᆫᄒ ᅮ ᅬᄀ ᅱᄆ ᅩᄒ ᆼᄋ ᅧ ᅦᄉ ᅥ ᅩᄒ ᄃ ᆨᅵ ᅪ ᄋᄒ ᆫ ᆯᄉ ᅡ ᅮᄋ ᆻᄋ ᅵ ᆻᄃ ᅥ ᅡ. ᄀ ᅳᄅ ᅥᄂ ᅡᄇ ᅦᄋ ᅵᄌ ᅵᄋ ᆫᄌ ᅡ ᅮᄉ ᆼᄇ ᅥ ᆫᄒ ᅮ ᅬᄀ ᅱᄆ ᅩᄒ ᆼᄋ ᅧ ᅦᄉ ᅥᄋ ᅴᄎ ᅮᄌ ᆼᄀ ᅥ ᆹᄋ ᅡ ᆫ 0ᄋ ᅳ ᅦᄀ ᅡᄁ ᅡᄋ ᆫᄀ ᅮ ᆹᄋ ᅡ ᆯᄇ ᅳ ᅩᄋ ᅵᄀ ᅩ 95% ᆫᄋ ᅵ ᄉ ᆼᅮ ᅭ ᄀᄀ ᆫ (credible interval)ᄋ ᅡ ᆫ 0ᄋ ᅳ ᆯM ᄑ ᅳ ᅩᄒ ᆷᄒ ᅡ ᅡᄀ ᅩᄋ ᆻᄋ ᅵ ᅥᄋ ᅲᄋ ᅴᄉ ᆼᄋ ᅥ ᆫᄆ ᅳ ᆫᄌ ᅡ ᆨᄒ ᅩ ᅡᄌ ᅵᄋ ᆭᄂ ᅡ ᆫᄃ ᅳ ᅡ. MCMCᄋ ᅦᄉ ᅥᄆ ᅦᄐ ᅳᄅ ᅩᄑ ᆯ ᅩ ᅵᄉ ᄅ ᅳ-ᄒ ᅢᄉ ᅳᄐ ᆼᄉ ᅵ ᅳᄋ ᆯᄀ ᅡ ᅩᄅ ᅵᄌ ᆷᅳ ᅳ ᆯ ᄋᄌ ᆨᄋ ᅥ ᆼᄒ ᅭ ᆫᄌ ᅡ ᅩᄋ ᆯᄆ ᅲ ᅩᄉ ᅮ rᄋ ᅴᄎ ᅮᄌ ᆼᄀ ᅥ ᆹᄋ ᅡ ᆫ 10.65ᄅ ᅳ ᅩᄒ ᆨᄋ ᅪ ᆫᄒ ᅵ ᆯᄉ ᅡ ᅮᄋ ᆻᄃ ᅵ ᅡ..
(9) 255. Bayesian PCA regression. 4. 2.5. lbph. e. 2.0 1.5. Variances. ag. 0 lcavol gleaso pgg45 n. lcp. 0.5. svi. 1.0. PC2 (20.7% explained var.). 2. lwei ght. 3.0. prostate.pca. −2. 1 −2. 0. 2. 2. 3. 4. 5. 6. 7. 8. 4. PC1 (41.4% explained var.). Figure 4.3 Biplot and scree plot of the prostate cancer data. 4.2. 전립선암 자료 ᅵᅡ ᄋ ᄌᄅ ᅭᄂ ᆫ Stamey ᄃ ᅳ ᆼ (1989) ᄌ ᅳ ᆫᄅ ᅥ ᆸᅥ ᅵ ᆫ ᄉᄋ ᆷᄌ ᅡ ᅡᄅ ᅭᄅ ᅩᄌ ᆫᄅ ᅥ ᆸᅥ ᅵ ᆫ ᄉᄐ ᆨᄋ ᅳ ᅵᄒ ᆼᄋ ᅡ ᆫᄋ ᅯ ᅴᄉ ᅮᄌ ᆫᄀ ᅮ ᅪᄀ ᆫᄎ ᅳ ᅵᄌ ᆨᅥ ᅥ ᆫ ᄌᄅ ᆸᅥ ᅵ ᆫ 서 ᆯ ᄌᄌ ᅦᄉ ᆯᄋ ᅮ ᆯᄋ ᅳ ᇁ ᅡ ᄃᄀ ᅮ ᅩᄋ ᆻᄂ ᅵ ᆫᄒ ᅳ ᆫᄌ ᅪ ᅡᄃ ᆯᄋ ᅳ ᅴᄋ ᆷᄉ ᅵ ᆼᄌ ᅡ ᅩᄉ ᅡᄌ ᅡᄅ ᅭᄋ ᅴᄀ ᆫᄀ ᅪ ᅨᄉ ᆼᄋ ᅥ ᆯᄋ ᅳ ᆫᄀ ᅧ ᅮᄒ ᅡᄀ ᅵᄋ ᅱᄒ ᅡᄋ ᅧᄌ ᅩᄉ ᅡᄒ ᆫᄌ ᅡ ᅡᄅ ᅭᄋ ᅵᄃ ᅡ. ᄌ ᆫᄎ ᅥ ᅦ 97ᄆ ᆼᄋ ᅧ ᅳᄅ ᅩᄇ ᅮᄐ ᅥ ᅩᄉ ᄌ ᅡᄒ ᆫᄉ ᅡ ᆯᅧ ᅥ ᆼ 며 ᆫ ᄇᄉ ᅮᄅ ᅩᄂ ᆫ ln(ᄋ ᅳ ᆷᄋ ᅡ ᅴᄇ ᅮᄑ ᅵ) (lcavol), ln(ᄌ ᆫᄅ ᅥ ᆸᄉ ᅵ ᆫᄆ ᅥ ᅮᄀ ᅦ) (lweight), ᄂ ᅡᄋ ᅵ (age), ln(ᄋ ᆼᄉ ᅣ ᆼᄌ ᅥ ᆫᄅ ᅥ ᆸᅥ ᅵ ᆫ ᄉᄇ ᅵ ᅢᄌ ᄃ ᆼᅴ ᅳ ᄋᄋ ᆼ) (lbph), ᄌ ᅣ ᆼᄂ ᅥ ᆼᄎ ᅡ ᆷᄇ ᅵ ᆷ (svi), ln(ᄑ ᅥ ᅵᄆ ᆨᄎ ᅡ ᆷᄐ ᅵ ᅮ) (lcp), ᄀ ᆯᄅ ᅳ ᅵᄉ ᆫᄌ ᅳ ᆷᄉ ᅥ ᅮ (gleason)ᄋ ᅪᄀ ᆯᄅ ᅳ ᅵᄉ ᆫᄌ ᅳ ᆷᄉ ᅥ ᅮ 4 ᄄ ᅩᄂ ᆫ ᅳ 5 ᄀ ᆹᄋ ᅡ ᅴᄇ ᆨᄇ ᅢ ᆫᅲ ᅮ ᆯ ᄋ (pgg45)ᄋ ᅵᄀ ᅩ, ᄇ ᆫᄋ ᅡ ᆼᄇ ᅳ ᆫᄉ ᅧ ᅮᄂ ᆫ ln(ᄌ ᅳ ᆫᄅ ᅥ ᆸᅥ ᅵ ᆫ ᄉᄐ ᆨᄋ ᅳ ᅵᄒ ᆼᄋ ᅡ ᆫᄋ ᅯ ᅴᄉ ᅮᄌ ᆫ)ᄋ ᅮ ᅵᄃ ᅡ. Tibshirani (1996), Zouᄋ ᅪ Hastie (2005)ᄂ ᆫ Lassoᄋ ᅳ ᅪ Elastic Netᄋ ᆯᄌ ᅳ ᆨᄋ ᅥ ᆼᄒ ᅭ ᅡᄋ ᅧᄋ ᅵᄌ ᅡᄅ ᅭᄅ ᆯᅮ ᅳ ᆫ ᄇᄉ ᆨᄒ ᅥ ᅡᄋ ᆻᄃ ᅧ ᅡ. ᆫᄅ ᅥ ᄌ ᆸᅥ ᅵ ᆫ ᄉᄋ ᆷᄌ ᅡ ᅡᄅ ᅭᄋ ᅴ 8ᄀ ᅢᄉ ᆯᅧ ᅥ ᆼ 며 ᆫ ᄇᄉ ᅮᄋ ᅦᄃ ᅢᄒ ᆫᄌ ᅡ ᅮᄉ ᆼᄇ ᅥ ᆫᅮ ᅮ ᆫ ᄇᄉ ᆨᄋ ᅥ ᅴᄒ ᆼᅧ ᅢ ᆯ ᄅᄃ ᅩ (biplot)ᄋ ᅪᄉ ᅳᄏ ᅳᄅ ᅵᄀ ᅳᄅ ᆷᄋ ᅵ ᆫᄀ ᅳ ᅳᄅ ᆷ 4.3ᄋ ᅵ ᅦᄉ ᅥ ᆨᄋ ᅪ ᄒ ᆫᄒ ᅵ ᆯᄉ ᅡ ᅮᄋ ᆻᄃ ᅵ ᅡ. ᄎ ᆺᅥ ᅥ ᆫ ᄇᄍ ᅢᄌ ᅮᄉ ᆼᄇ ᅥ ᆫᄋ ᅮ ᆫᄌ ᅳ ᆫᄎ ᅥ ᅦᄇ ᆫᄉ ᅮ ᆫᄋ ᅡ ᅴ 41.4%ᄅ ᆯᄉ ᅳ ᆯᅧ ᅥ ᆼ ᄆᄒ ᅡᄆ ᅧ, ln(ᄋ ᆷᄋ ᅡ ᅴᄇ ᅮᄑ ᅵ), ᄌ ᆼᄂ ᅥ ᆼᄎ ᅡ ᆷᄇ ᅵ ᆷ, ln(ᄑ ᅥ ᅵᄆ ᆨᄎ ᅡ ᆷ ᅵ ᅮ), ᄀ ᄐ ᆯᄅ ᅳ ᅵᄉ ᆫᄌ ᅳ ᆷᄉ ᅥ ᅮᅪ ᄋᄀ ᆯᄅ ᅳ ᅵᄉ ᆫᄌ ᅳ ᆷᄉ ᅥ ᅮ4ᄄ ᅩᄂ ᆫ5ᄀ ᅳ ᆹᄋ ᅡ ᅴᄇ ᆨᄇ ᅢ ᆫᅲ ᅮ ᆯ ᄋᄇ ᆫᄉ ᅧ ᅮᄋ ᅴᄀ ᅡᄌ ᆼᄑ ᅮ ᆼᄀ ᅧ ᆫᄋ ᅲ ᅴᄌ ᅮᄉ ᆼᄇ ᅥ ᆫᄋ ᅮ ᅵᄅ ᅡᄒ ᆯᄉ ᅡ ᅮᄋ ᆻᄀ ᅵ ᅩ, ᄃ ᅮᄇ ᆫ ᅥ ᅢᄌ ᄍ ᅮᄉ ᆼᄇ ᅥ ᆫᄋ ᅮ ᆫᄌ ᅳ ᆫᄎ ᅥ ᅦᄇ ᆫᄉ ᅮ ᆫᄋ ᅡ ᅴ 20.7%ᄅ ᆯᄉ ᅳ ᆯᅧ ᅥ ᆼ ᄆᄒ ᅡᄆ ᅧ, ln(ᄌ ᆫᄅ ᅥ ᆸᅥ ᅵ ᆫ ᄉᄆ ᅮᄀ ᅦ), ᄂ ᅡᄋ ᅵ, ln(ᄋ ᆼᄉ ᅣ ᆼᅥ ᅥ ᆫ ᄌᄅ ᆸᅥ ᅵ ᆫ ᄉᄇ ᅵᄃ ᅢᄌ ᆼᄋ ᅳ ᅴᅣ ᆼ ᄋ)ᄋ ᅴᄀ ᅡ ᆼᄑ ᅮ ᄌ ᆼᄀ ᅧ ᆫᄋ ᅲ ᅴᄌ ᅮᄉ ᆼᄇ ᅥ ᆫᄋ ᅮ ᅵᄅ ᅡᄒ ᆯᄉ ᅡ ᅮᄋ ᆻᄃ ᅵ ᅡ. ᄃ ᅮᄌ ᅮᄉ ᆼᄇ ᅥ ᆫᄋ ᅮ ᅴᄂ ᅮᄌ ᆨᄇ ᅥ ᆫᄉ ᅮ ᆫᄋ ᅡ ᆫᄌ ᅳ ᆫᄎ ᅥ ᅦᄇ ᆫᄉ ᅮ ᆫᄋ ᅡ ᅴ 62.1%ᄀ ᅡᅬ ᄃᄌ ᅵᄆ ᆫᄉ ᅡ ᅳᄏ ᅳᄅ ᅵᄀ ᅳᄅ ᆷᄋ ᅵ ᆯ ᅳ ᆨᄋ ᅪ ᄒ ᆫᅢ ᅵ ᄒᄇ ᅩᄆ ᆫᅥ ᅧ ᆫ ᄌᄎ ᅦᄉ ᆯᅧ ᅥ ᆼ ᄆᄇ ᆫᄉ ᅧ ᅮᄋ ᅴᄀ ᆺᄉ ᅢ ᅮ p = 8ᄁ ᅡᄌ ᅵᄀ ᅨᄉ ᆨᄀ ᅩ ᆷᄉ ᅡ ᅩᄒ ᅡᄂ ᆫᄑ ᅳ ᅢᄐ ᆫᄋ ᅥ ᆯᄇ ᅳ ᅩᄋ ᅵᄀ ᅩᄋ ᆻᄃ ᅵ ᅡ. ᅮᄉ ᄌ ᆼᄇ ᅥ ᆫᄀ ᅮ ᆺᄉ ᅢ ᅮᄋ ᅦᄃ ᅢᄒ ᆫ BIC ᄀ ᅡ ᅳᄅ ᆷᄋ ᅵ ᆫᄀ ᅵ ᅳᄅ ᆷ 4.4ᄅ ᅵ ᆯᄇ ᅳ ᅩᄆ ᆫ BIC ᄎ ᅧ ᅬᄉ ᆺᄀ ᅩ ᆹᄋ ᅡ ᆫ K = 5ᄋ ᅳ ᆯᄄ ᅵ ᅢᄋ ᅵᄆ ᅧ, ᄂ ᅮᄌ ᆨᄇ ᅥ ᆫᄉ ᅮ ᆫᄋ ᅡ ᅵᄌ ᆫ ᅥ ᅦᄇ ᄎ ᆫᄉ ᅮ ᆫᄋ ᅡ ᅴ 88.45%ᄀ ᅡᄃ ᆫᄃ ᅬ ᅡ. K = 5ᄀ ᅢᄋ ᅴᄌ ᅮᄉ ᆼᄇ ᅥ ᆫᅳ ᅮ ᆯ ᄋᄒ ᅬᄀ ᅱᄇ ᆫᄉ ᅧ ᅮᄅ ᅩᄉ ᅡᄋ ᆼᄒ ᅭ ᆫᄇ ᅡ ᅦᄋ ᅵᄌ ᅵᄋ ᆫᄌ ᅡ ᅮᄉ ᆼᄇ ᅥ ᆫᄒ ᅮ ᅬᄀ ᅱᄆ ᅩᄒ ᆼᄋ ᅧ ᅴ θᄋ ᅴᄎ ᅮ ᆼᄀ ᅥ ᄌ ᆹᄋ ᅡ ᆯᄇ ᅳ ᅩᄆ ᆫᄃ ᅧ ᅮᄇ ᆫᄍ ᅥ ᅢᄌ ᅮᄉ ᆼᄇ ᅥ ᆫᅳ ᅮ ᆯ ᄋᄌ ᅦᅬ 아 ᆫ ᄒᄂ ᅡᄆ ᅥᄌ ᅵ 4ᄀ ᅢᄋ ᅴᄌ ᅮᄉ ᆼᄇ ᅥ ᆫᄆ ᅮ ᅩᄉ ᅮᄋ ᅴ 95% ᄉ ᆫᄋ ᅵ ᆼᄀ ᅭ ᅮᄀ ᆫ (credible interval)ᄋ ᅡ ᆫ ᅳ 0ᄋ ᆯᅩ ᅳ ᄑᄒ ᆷᄒ ᅡ ᅡᄀ ᅩᄋ ᆻᄌ ᅵ ᅵᄋ ᆭᄃ ᅡ ᅡ. MCMC ᄋ ᆯᄀ ᅡ ᅩᄅ ᅵᄌ ᆷᄋ ᅳ ᅴᄉ ᅮᄅ ᆷᅥ ᅧ ᆼ ᄉᄋ ᆯᄒ ᅳ ᆨᄋ ᅪ ᆫᄒ ᅵ ᅡᄀ ᅵᄋ ᅱᅡ ᆫ ᄒᄎ ᅮᄎ ᆨᄀ ᅥ ᅳᄅ ᆷ (traceplot)ᄋ ᅵ ᆫᄇ ᅳ ᅮᄅ ᆨᄋ ᅩ ᅦ T ᅥᄒ ᄉ ᆨᄋ ᅪ ᆫᄒ ᅵ ᆯᄉ ᅡ ᅮᄋ ᆻᄃ ᅵ ᅡ. ᄋ ᆫᄅ ᅯ ᅢᄋ ᅴᄉ ᆯᅧ ᅥ ᆼ 며 ᆫ ᄇᄉ ᅮᄋ ᅴᄆ ᅩᄉ ᅮ βᄅ ᅩᄋ ᆨᅧ ᅧ ᆫ ᄇᄒ ᆫᄒ ᅪ ᆫ β = A θᄋ ᅡ ᅴᄎ ᅮᄌ ᆼᄀ ᅥ ᆹᄋ ᅡ ᆯᄉ ᅳ ᆯᄑ ᅡ ᅧᄇ ᅩᄆ ᆫᄂ ᅧ ᅡᄋ ᅵ, ᄌ ᆼᄂ ᅥ ᆼ ᅡ ᆷᄇ ᅵ ᄎ ᆷ, ln(ᄑ ᅥ ᅵᄆ ᆨᄎ ᅡ ᆷᄐ ᅵ ᅮ), ᄀ ᆯᄅ ᅳ ᅵᄉ ᆫᄌ ᅳ ᆷᄉ ᅥ ᅮ4ᄄ ᅩᄂ ᆫ5ᄀ ᅳ ᆹᄋ ᅡ ᅴᄇ ᆨᄇ ᅢ ᆫᅲ ᅮ ᆯ ᄋᄇ ᆫᄉ ᅧ ᅮᄋ ᅴ 95% ᄉ ᆫᄋ ᅵ ᆼᄀ ᅭ ᅮᄀ ᆫ (credible interval)ᄋ ᅡ ᆫ 0ᄋ ᅳ ᆯ ᅳ ᅩᄒ ᄑ ᆷᅡ ᅡ ᄒᄀ ᅩᄋ ᆻᄀ ᅵ ᅩ, ᄂ ᅡᄆ ᅥᄌ ᅵᄇ ᆫᄉ ᅧ ᅮᄃ ᆯᄋ ᅳ ᅴᄉ ᆫᄋ ᅵ ᆼᄀ ᅭ ᅮᄀ ᆫᄋ ᅡ ᆫ 0ᄋ ᅳ ᆯᄑ ᅳ ᅩᄒ ᆷᄒ ᅡ ᅡᄌ ᅵᄋ ᆭᄂ ᅡ ᆫᄃ ᅳ ᅡ. ᄀ ᅳᄅ ᅵᄀ ᅩ ln(ᄑ ᅵᄆ ᆨᄎ ᅡ ᆷᄐ ᅵ ᅮ)ᄇ ᆫᄉ ᅧ ᅮᄋ ᅴᄉ ᅡᄒ ᅮᄇ ᆫ ᅮ ᆫᄎ ᅡ ᄉ ᅮᄌ ᆼᄀ ᅥ ᆹᄋ ᅡ ᆫᄆ ᅳ ᅢᄋ ᅮᄏ ᆫᄀ ᅳ ᆺᄋ ᅥ ᆯᄒ ᅳ ᆨᄋ ᅪ ᆫᄒ ᅵ ᆯᄉ ᅡ ᅮᄋ ᆻᄃ ᅵ ᅡ. ᄋ ᅵᄌ ᅡᄅ ᅭᄋ ᅴᄌ ᅩᄋ ᆯᄆ ᅲ ᅩᄉ ᅮ rᄋ ᅴᄎ ᅮᄌ ᆼᄀ ᅥ ᆹᄋ ᅡ ᆫ 8ᄅ ᅳ ᅩᄉ ᅮᄅ ᆷᄒ ᅧ ᆷᄋ ᅡ ᆯᄒ ᅳ ᆨᄋ ᅪ ᆫᄒ ᅵ ᆯᄉ ᅡ ᅮ ᆻᄋ ᅵ ᄋ ᆻᅡ ᅥ ᄃ. Tibshirani (1996)ᄋ ᅴᄋ ᆫᄀ ᅧ ᅮᄋ ᅦᄉ ᅥ Lassoᄅ ᆯᄌ ᅳ ᆨᄋ ᅥ ᆼᄒ ᅭ ᆫᅧ ᅡ ᆼ ᄀᄋ ᅮ ln(ᄋ ᆷᄋ ᅡ ᅴᄇ ᅮᄑ ᅵ) (lcavol), ln(ᄌ ᆫᄅ ᅥ ᆸᄉ ᅵ ᆫᄆ ᅥ ᅮᄀ ᅦ) (lweight), ln(ᄋ ᆼᄉ ᅣ ᆼᅥ ᅥ ᆫ ᄌᄅ ᆸᅥ ᅵ ᆫ ᄉᄇ ᅵᄃ ᅢᄌ ᆼᄋ ᅳ ᅴᅣ ᆼ ᄋ) (lbph), ᄌ ᆼᄂ ᅥ ᆼᄎ ᅡ ᆷᄇ ᅵ ᆷ (svi)ᄋ ᅥ ᅪᄀ ᆯᄅ ᅳ ᅵᄉ ᆫᄌ ᅳ ᆷᄉ ᅥ ᅮ4ᄄ ᅩᄂ ᆫ5ᄀ ᅳ ᆹᄋ ᅡ ᅴᄇ ᆨᄇ ᅢ ᆫᄋ ᅮ ᆯ (pgg45)ᄇ ᅲ ᆫᄉ ᅧ ᅮ ᆯᄋ ᅳ ᄃ ᅵᅬ ᄎᄌ ᆼᄆ ᅩ ᅩᄒ ᆼᄋ ᅧ ᅦᄉ ᆫᅢ ᅥ ᆨ ᄐᄃ ᅬᄋ ᆻᄀ ᅥ ᅩ, Zouᄋ ᅪ Hastie (2005)ᄋ ᅴ Elastic Netᄋ ᆯᄌ ᅳ ᆨᄋ ᅥ ᆼᄒ ᅭ ᆫᅧ ᅡ ᆼ ᄀᄋ ᅮ ln(ᄋ ᆷᄋ ᅡ ᅴᄇ ᅮᄑ ᅵ) (lcavol), ln(ᄌ ᆫᄅ ᅥ ᆸᅥ ᅵ ᆫ ᄉᄆ ᅮᄀ ᅦ) (lweight), ᄌ ᆼᄂ ᅥ ᆼᄎ ᅡ ᆷᄇ ᅵ ᆷ (svi), ln(ᄑ ᅥ ᅵᄆ ᆨᄎ ᅡ ᆷᄐ ᅵ ᅮ) (lcp) ᄀ ᆯᄅ ᅳ ᅵᄉ ᆫ ᄌ ᅳ ᆷᄉ ᅥ ᅮ 4 ᄄ ᅩᄂ ᆫ 5 ᄀ ᅳ ᆹᄋ ᅡ ᅴ ᄇ ᆨᄇ ᅢ ᆫᄋ ᅮ ᆯ ᅲ (pgg45)ᄋ ᅵ ᅬ ᄎᄌ ᆼᄆ ᅩ ᅩᄒ ᆼᄋ ᅧ ᅦ ᄉ ᆫᅢ ᅥ ᆨ ᄐᄃ ᅬᄋ ᆻᄃ ᅥ ᅡ. ᄇ ᅦᄋ ᅵᄌ ᅵᄋ ᆫ ᄌ ᅡ ᅮᄉ ᆼᄇ ᅥ ᆫ ᄒ ᅮ ᅬᄀ ᅱᄆ ᅩᄒ ᆼᄋ ᅧ ᆯ ᄌ ᅳ ᆨᄋ ᅥ ᆼᄒ ᅭ ᅡᄋ ᆻᄋ ᅧ ᆯ ᄄ ᅳ ᅢ ᅬ ᄎᄌ ᆼᄌ ᅩ ᆨᄋ ᅥ ᅳᄅ ᅩ ᄉ ᆫᅢ ᅥ ᆨ ᄐᄃ ᅬᄂ ᆫ ᅳ ᆫᄉ ᅧ ᄇ ᅮᄂ ᆫ ln(ᄋ ᅳ ᆷᄋ ᅡ ᅴᄇ ᅮᄑ ᅵ) (lcavol), ln(ᄌ ᆫᄅ ᅥ ᆸᅥ ᅵ ᆫ ᄉᄆ ᅮᄀ ᅦ) (lweight), ln(ᄋ ᆼᄉ ᅣ ᆼᄌ ᅥ ᆫᄅ ᅥ ᆸᅥ ᅵ ᆫ ᄉᄇ ᅵᄃ ᅢᄌ ᆼᄋ ᅳ ᅴᄋ ᆼ) (lbph), ᄀ ᅣ ᆯᄅ ᅳ ᅵᄉ ᆫ ᅳ.
(10) 256. Minjung Kyung. 0.6. −40000. 0.2. 95% credible intervals. −60000. 0.0. BIC. 0.4. −50000. −0.2. −70000. −80000. lcavol 2. 4. 6. 8. K. lweight. age. lbph. svi. lcp. gleason. pgg45. betas. Figure 4.4 BIC plot of the number of principal components and estimates of wavelength regression coefficients β for the prostate cancer data. ᆷᄉ ᅥ ᄌ ᅮ (gleason)ᄋ ᅵᄃ ᅡ. ᄀ ᅳᄅ ᅥᄂ ᅡᄌ ᆼᄂ ᅥ ᆼᄎ ᅡ ᆷᄇ ᅵ ᆷ (svi)ᄋ ᅥ ᅴᄀ ᆼᄋ ᅧ ᅮᄀ ᅳᄅ ᆷ 4.4ᄋ ᅵ ᅴ 95% ᄉ ᆫᄋ ᅵ ᆼᄀ ᅭ ᅮᄀ ᆫ (credible interval)ᄋ ᅡ ᆯᄉ ᅳ ᆯ ᅡ ᄑᄇ ᅧ ᅩᄆ ᆫ, ᄉ ᅧ ᆫᄋ ᅵ ᆼᄀ ᅭ ᅮᄀ ᆫᄋ ᅡ ᅴᄎ ᅬᄉ ᅩᄀ ᆹᄋ ᅡ ᅵ −0.005ᄅ ᅩᄀ ᅥᄋ ᅴ 0ᄋ ᅦᄀ ᆫᄉ ᅳ ᅡᄒ ᆷᄋ ᅡ ᆯᄒ ᅳ ᆨᄋ ᅪ ᆫᄒ ᅵ ᆯᄉ ᅡ ᅮᄋ ᆻᄃ ᅵ ᅡ. ᄀ ᅳᄅ ᅥᄆ ᅳᄅ ᅩᄋ ᅨᄎ ᆨᄌ ᅳ ᆼᄒ ᅥ ᆨᄃ ᅪ ᅩᄅ ᆯᄂ ᅳ ᇁ ᅩ ᅵᄀ ᄋ ᅵᅱ 아 ᆫ ᄒᄃ ᅢᄋ ᆫᄋ ᅡ ᅳᄅ ᅩᄌ ᆼᄂ ᅥ ᆼᄎ ᅡ ᆷᄇ ᅵ ᆷ (svi)ᄋ ᅥ ᆯᄎ ᅳ ᅬᄌ ᆼᄆ ᅩ ᅩᄒ ᆼᄋ ᅧ ᅦᄑ ᅩᄒ ᆷᄒ ᅡ ᆯᄉ ᅡ ᅮᄋ ᆻᄋ ᅵ ᅳᄆ ᅧ, ᄇ ᅦᄋ ᅵᄌ ᅵᄋ ᆫᄌ ᅡ ᅮᄉ ᆼᄇ ᅥ ᆫᄒ ᅮ ᅬᄀ ᅱᄆ ᅩᄒ ᆼᄋ ᅧ ᆯᄌ ᅳ ᆨᄋ ᅥ ᆼ ᅭ ᆫᅧ ᅡ ᄒ ᆼ 구 ᄋ Lassoᄋ ᅴᄇ ᆫᄉ ᅧ ᅮᄉ ᆫᅢ ᅥ ᆨ 텨 ᆯ ᄀᄀ ᅪᄋ ᅪᄇ ᅵᄉ ᆺᄒ ᅳ ᅡᄃ ᅡᄀ ᅩᄒ ᆯᄉ ᅡ ᅮᄋ ᆻᄃ ᅵ ᅡ.. 5. 결론 ᆫᅧ ᅥ ᄉ ᆼ ᄒᄆ ᅩᄒ ᆼᄋ ᅧ ᅦᄉ ᅥᄆ ᅩᄉ ᅮᄋ ᅴᄎ ᅮᄅ ᆫᄆ ᅩ ᆾᄆ ᅵ ᅩᄒ ᆼᄋ ᅧ ᅴᄌ ᆨᄒ ᅥ ᆸᅥ ᅡ ᆼ ᄉᄋ ᅦᄆ ᆫᄌ ᅮ ᅦᄀ ᅡᄃ ᅬᄂ ᆫᄉ ᅳ ᆯᅧ ᅥ ᆼ 며 ᆫ ᄇᄉ ᅮᄋ ᅴᄀ ᆺᄉ ᅢ ᅮᄀ ᅡᄀ ᆫᄎ ᅪ ᆨᄀ ᅳ ᆺᄉ ᅢ ᅮᄇ ᅩᄃ ᅡᄆ ᆭᄋ ᅡ ᆫᄌ ᅳ ᅡ ᄅ (p > n)ᄋ ᅭ ᅪᄃ ᅮᄀ ᅢᄋ ᅵᄉ ᆼᄋ ᅡ ᅴᄉ ᆯᅧ ᅥ ᆼ 며 ᆫ ᄇᄉ ᅮᄃ ᆯᄉ ᅳ ᅡᄋ ᅵᄋ ᅦᄌ ᆫᄌ ᅩ ᅢᄒ ᅡᄂ ᆫᄃ ᅳ ᅡᄌ ᆼᄀ ᅮ ᆼᄉ ᅩ ᆫᅥ ᅥ ᆼ ᄉ (multicollinearity)ᄆ ᆫᄌ ᅮ ᅦᄅ ᆯᄉ ᅳ ᅥᄅ ᅩᄌ ᆨ ᅵ ᅭᄀ ᄀ ᅡᅬ ᄃᄂ ᆫᄌ ᅳ ᅮᄉ ᆼᄇ ᅥ ᆫᄋ ᅮ ᆯᄒ ᅳ ᅬᄀ ᅱᄇ ᆫᄉ ᅧ ᅮᄅ ᅩᄉ ᅡᄋ ᆼᄒ ᅭ ᅡᄋ ᅧᄒ ᅢᄀ ᆯᄒ ᅧ ᅡᄂ ᆫᄌ ᅳ ᅮᄉ ᆼᄇ ᅥ ᆫᄒ ᅮ ᅬᄀ ᅱᄆ ᅩᄒ ᆼᄋ ᅧ ᅦᄃ ᅢᄒ ᅡᄋ ᅧᄂ ᆫᄋ ᅩ ᅴᄒ ᅡᄋ ᆻᄃ ᅧ ᅡ. ᄉ ᆯᅧ ᅥ ᆼ 며 ᆫ ᄇᄉ ᅮᄃ ᆯ ᅳ ᅴᄌ ᄋ ᅮᄉ ᆼᄇ ᅥ ᆫᅳ ᅮ ᆯ ᄋᄋ ᆮᄀ ᅥ ᅵᄋ ᅱᄒ ᅡᄋ ᅧ, ᄌ ᅮᄉ ᆼᄇ ᅥ ᆫᅮ ᅮ ᆫ ᄇᄉ ᆨᄋ ᅥ ᆯᄉ ᅳ ᅵᄒ ᆼᄒ ᅢ ᅡᄂ ᆫᄃ ᅳ ᅢᄉ ᆫᅥ ᅵ ᆯ 셔 ᆼ 며 ᆫ ᄇᄉ ᅮᄒ ᆼᅧ ᅢ ᆯ ᄅᄋ ᅴᄐ ᆨᄋ ᅳ ᅵᄀ ᆹᄇ ᅡ ᆫᄒ ᅮ ᅢᄅ ᆯᄐ ᅳ ᆼᄒ ᅩ ᆫᄌ ᅡ ᆨᄀ ᅵ ᅭᄉ ᆼᄋ ᅥ ᆯ ᅳ ᆫᄌ ᅡ ᄆ ᆨᅡ ᅩ ᄒᄂ ᆫᄒ ᅳ ᅬᄀ ᅱᄇ ᆫᄉ ᅮ ᅮᄅ ᆯᄎ ᅳ ᆽᄂ ᅡ ᆫᄇ ᅳ ᆼᄇ ᅡ ᆸᄋ ᅥ ᆯᄉ ᅳ ᅡᄋ ᆼᄒ ᅭ ᅡᄋ ᆻᄃ ᅧ ᅡ. ᄎ ᅡᄋ ᆫᄎ ᅯ ᆨᄉ ᅮ ᅩᄋ ᅴᄆ ᆫᄌ ᅮ ᅦᅪ ᄋᄆ ᅩᄉ ᅮᄎ ᅮᄌ ᆼᄋ ᅥ ᅴᄆ ᆫᄌ ᅮ ᅦᄅ ᆯᄃ ᅳ ᆼᄉ ᅩ ᅵᄋ ᅦᄉ ᅮᄒ ᆼᄒ ᅢ ᆯ ᅡ ᅮᄋ ᄉ ᆻᄂ ᅵ ᆫᄉ ᅳ ᆫᅧ ᅥ ᆼ ᄒᄆ ᅩᄒ ᆼᄋ ᅧ ᅳᄅ ᅩᄋ ᅴᄆ ᅵᄀ ᅡᄋ ᆻᄌ ᅵ ᅵᄆ ᆫ, ᄇ ᅡ ᆫᄋ ᅡ ᆼᄇ ᅳ ᆫᄉ ᅧ ᅮᄋ ᅴᄋ ᅨᄎ ᆨᅳ ᅳ ᆯ ᄋᄆ ᆨᄌ ᅩ ᆨᄋ ᅥ ᅳᄅ ᅩᄒ ᅡᄂ ᆫᅮ ᅳ ᆫ ᄇᄉ ᆨᄋ ᅥ ᅦᄉ ᅥᄂ ᆫᄆ ᅳ ᅩᄉ ᅮᄎ ᅮᄌ ᆼᄋ ᅥ ᅦᄉ ᅡᄋ ᆼᄒ ᅭ ᅡ ᅵᄋ ᄌ ᆭᄂ ᅡ ᆫᅮ ᅳ ᆫ ᄇᄉ ᆫᄋ ᅡ ᅵᄌ ᆨᄋ ᅡ ᆫᄌ ᅳ ᅮᄉ ᆼᄇ ᅥ ᆫᄋ ᅮ ᆨᄉ ᅧ ᅵᄋ ᅨᄎ ᆨᄋ ᅳ ᅦᄉ ᅥᄂ ᆫᅮ ᅳ ᆼ ᄌᄋ ᅭᄒ ᆫᄋ ᅡ ᆨᄒ ᅧ ᆯᄋ ᅡ ᆯᄒ ᅳ ᆯᄉ ᅡ ᅮᄋ ᆻᄃ ᅵ ᅡᄂ ᆫᅮ ᅳ ᆫ ᄆᄌ ᅦᄀ ᅡᄌ ᅦᄀ ᅵᄃ ᆫᄃ ᅬ ᅡᄂ ᆫᄃ ᅳ ᆫᄌ ᅡ ᆷᄋ ᅥ ᅵᄋ ᆻ ᅵ ᅡ. ᄃ ᅵᄂ ᄋ ᆫᅮ ᅩ ᆫ ᄆᄋ ᅦᄉ ᅥᄌ ᅦᄉ ᅵᄒ ᆫᄌ ᅡ ᅮᄉ ᆼᄇ ᅥ ᆫᄒ ᅮ ᅬᄀ ᅱᄆ ᅩᄒ ᆼᄋ ᅧ ᅴᄆ ᅩᄉ ᅮᄎ ᅮᄌ ᆼᄇ ᅥ ᆼᄇ ᅡ ᆸᄋ ᅥ ᅳᄅ ᅩᄋ ᆯᄇ ᅵ ᆫᄌ ᅡ ᆨᄋ ᅥ ᆫᄎ ᅵ ᆨᄉ ᅮ ᅩᄉ ᅡᄌ ᆫᄇ ᅥ ᆫᄑ ᅮ ᅩᄅ ᆯᄉ ᅳ ᅡᄋ ᆼᄒ ᅭ ᅡᄋ ᅧᄋ ᆫᄌ ᅵ ᅡ ᅬᄀ ᄒ ᅱᄆ ᅩᄉ ᅮᄋ ᅪᄇ ᆫᄉ ᅮ ᆫᄋ ᅡ ᅦᄋ ᆫᄀ ᅧ ᆫᄃ ᅪ ᆫᄆ ᅬ ᅩᄉ ᅮᄃ ᆯᅳ ᅳ ᆯ ᄋᄎ ᅮᄌ ᆼᄒ ᅥ ᆫᄒ ᅡ ᅮᄃ ᅡᄉ ᅵᄋ ᆫᄒ ᅯ ᅬᄀ ᅱᄀ ᅨᄉ ᅮᄅ ᅩᄋ ᆨᅧ ᅧ ᆫ ᄇᄒ ᆫᄒ ᅪ ᅡᄂ ᆫᄉ ᅳ ᅡᄒ ᅮᄑ ᅭᄇ ᆫᄎ ᅩ ᅮᄎ ᆯᄀ ᅮ ᅪᄌ ᆼᄋ ᅥ ᆯ ᅳ ᅡᄋ ᄉ ᆼᄒ ᅭ ᆫᄇ ᅡ ᅦᄋ ᅵᄌ ᅵᄋ ᆫᄇ ᅡ ᆫᄉ ᅮ ᆨᅥ ᅥ ᆸ ᄇᄋ ᆯᄌ ᅳ ᆨᄋ ᅥ ᆼᄒ ᅭ ᅡᄋ ᆻᄃ ᅧ ᅡ. ᄀ ᅳᄅ ᅵᄀ ᅩᄌ ᅮᄉ ᆼᄇ ᅥ ᆫᄋ ᅮ ᅴᄀ ᆺᄉ ᅢ ᅮᄌ ᆨᄒ ᅳ ᅬᄀ ᅱᄇ ᆫᄉ ᅧ ᅮᄋ ᅴᄀ ᆺᄉ ᅢ ᅮᄅ ᆯᄀ ᅳ ᆯᄌ ᅧ ᆼᄒ ᅥ ᅡᄂ ᆫᄇ ᅳ ᆼᄇ ᅡ ᆸᄋ ᅥ ᅳᄅ ᅩ ᆫᄋ ᅡ ᄇ ᆼᄇ ᅳ ᆫᄉ ᅧ ᅮᅪ ᄋᄋ ᅴᄀ ᆫᄀ ᅪ ᅨᄅ ᆯᄀ ᅳ ᅩᄅ ᅧᄒ ᅡᄋ ᅧᄇ ᅦᄋ ᅵᄌ ᅳᄌ ᆼᄇ ᅥ ᅩᄀ ᅵᄌ ᆫᄋ ᅮ ᅦᄀ ᆫᄀ ᅳ ᅥᄒ ᆫᄇ ᅡ ᆼᄇ ᅡ ᆸᄋ ᅥ ᆯᄌ ᅳ ᅦᄉ ᅵᄒ ᅡᄋ ᆻᄃ ᅧ ᅡ. ᅦᅵ ᄌ ᄉᄒ ᆫ ᄇ ᅡ ᆼᄇ ᅡ ᆸᄋ ᅥ ᆯ ᄌ ᅳ ᆨᄋ ᅥ ᆼᄒ ᅭ ᆫ ᄌ ᅡ ᅡᄅ ᅭᄇ ᆫᄉ ᅮ ᆨ ᄀ ᅥ ᆯᄀ ᅧ ᅪ ᄉ ᆯᅧ ᅥ ᆼ 며 ᆫ ᄇᄉ ᅮᄋ ᅴ ᄀ ᆺᄉ ᅢ ᅮᄀ ᅡ ᄀ ᆫᄎ ᅪ ᆨᄀ ᅳ ᆺᄉ ᅢ ᅮ ᄇ ᅩᄃ ᅡ ᄆ ᆭᄋ ᅡ ᆫ ᄏ ᅳ ᅮᄏ ᅵᄇ ᆫᄌ ᅡ ᆨᄌ ᅮ ᅡᄅ ᅭᄋ ᅴ ᄀ ᆼᄋ ᅧ ᅮ 12ᄀ ᅢᅴ ᄋᄌ ᅮᄉ ᆼᄇ ᅥ ᆫᅳ ᅮ ᆯ ᄋᄉ ᆫᄐ ᅥ ᆨᄒ ᅢ ᆫᄆ ᅡ ᅩᄒ ᆼᄋ ᅧ ᅴ BICᄀ ᆹᄋ ᅡ ᅵᄀ ᅡᄌ ᆼᄌ ᅡ ᆨᄋ ᅡ ᆻᄀ ᅡ ᅩ, 12ᄀ ᅢᄋ ᅴᄌ ᅮᄉ ᆼᄇ ᅥ ᆫᅳ ᅮ ᆯ ᄋᄒ ᅬᄀ ᅱᄇ ᆫᄉ ᅧ ᅮᄅ ᅩᄉ ᅡᄋ ᆼᄒ ᅭ ᆫᄌ ᅡ ᅮᄉ ᆼᄇ ᅥ ᆫᄒ ᅮ ᅬ ᅱᄆ ᄀ ᅩᄒ ᆼᄋ ᅧ ᅦᄉ ᅥᄇ ᅦᄋ ᅵᄌ ᅵᄋ ᆫᄇ ᅡ ᆫᄉ ᅮ ᆨᅧ ᅥ ᆯ ᄀᄀ ᅪᄇ ᆫᄉ ᅮ ᆫᄋ ᅡ ᅵᄀ ᅡᄌ ᆼᄏ ᅡ ᆫᄃ ᅳ ᅮᄀ ᅢᄋ ᅴᄌ ᅮᄉ ᆼᄇ ᅥ ᆫᄋ ᅮ ᅦᄃ ᅢᄒ ᆫᄆ ᅡ ᅩᄉ ᅮᄆ ᆫᄋ ᅡ ᅵᄋ ᅲᄋ ᅴᅡ ᆫ ᄒᄀ ᆹᄋ ᅡ ᆯᄀ ᅳ ᆽᄂ ᅡ ᆫᄀ ᅳ ᆺᄋ ᅥ ᆯ ᅳ ᆨᄋ ᅪ ᄒ ᆫᄒ ᅵ ᆯᄉ ᅡ ᅮᄋ ᆻᄋ ᅵ ᆻᄃ ᅥ ᅡ. ᄀ ᅳᄅ ᅥᄂ ᅡᄋ ᅨᄎ ᆨᄋ ᅳ ᅴᄀ ᆫᄌ ᅪ ᆷᄋ ᅥ ᅦᄉ ᅥᄉ ᆯᄑ ᅡ ᅧᄇ ᅩᄆ ᆫ 12ᄀ ᅧ ᅢᄋ ᅴᄌ ᅮᄉ ᆼᄇ ᅥ ᆫᄋ ᅮ ᆯᄉ ᅳ ᅡᄋ ᆼᄒ ᅭ ᆫᄒ ᅡ ᅬᄀ ᅱᄆ ᅩᄒ ᆼᄋ ᅧ ᅵᅬ ᄎᄌ ᆼᄆ ᅩ ᅩᄒ ᆼᄋ ᅧ ᅵᄅ ᅡ ᆯᄉ ᅡ ᄒ ᅮᄋ ᆻᄀ ᅵ ᅩ, ᄋ ᅵᄅ ᆯᄇ ᅳ ᅡᄐ ᆼᄋ ᅡ ᅳᄅ ᅩᄋ ᆮᄋ ᅥ ᆫᄋ ᅳ ᆫᄌ ᅵ ᅡᄒ ᅬᄀ ᅱᄆ ᅩᄉ ᅮᄋ ᅴᄉ ᅡᄒ ᅮᄇ ᆫᄑ ᅮ ᅩᄅ ᆯᄃ ᅳ ᅡᄉ ᅵᄋ ᆨᄇ ᅧ ᆫᄒ ᅧ ᆫᄒ ᅪ ᅡᄋ ᅧᄋ ᆫᄒ ᅯ ᅬᄀ ᅱᄆ ᅩᄉ ᅮᄋ ᅴᄉ ᅡᄒ ᅮᄇ ᆫᄑ ᅮ ᅩ ᆯᄋ ᅳ ᄅ ᆮᅥ ᅥ ᆻ ᄋᄃ ᅡ. ᄀ ᅵᄌ ᆫᄋ ᅩ ᅴᄋ ᆫᄀ ᅧ ᅮᄋ ᅦᄉ ᅥᄇ ᆫᄉ ᅧ ᅮᄉ ᆫᄐ ᅥ ᆨᄆ ᅢ ᆫᄌ ᅮ ᅦᄋ ᅦᄌ ᆨᄋ ᅥ ᆼᄃ ᅭ ᆫᄌ ᅬ ᆫᄅ ᅥ ᆸᅥ ᅵ ᆫ ᄉᄋ ᆷᄌ ᅡ ᅡᄅ ᅭᄋ ᅴᄀ ᆼᄋ ᅧ ᅮ, ᄌ ᅦᄉ ᅵᄒ ᆫᄌ ᅡ ᅮᄉ ᆼᄇ ᅥ ᆫᄒ ᅮ ᅬᄀ ᅱᄆ ᅩᄒ ᆼᄋ ᅧ ᆯ ᅳ ᆨᄒ ᅥ ᄌ ᆸᄒ ᅡ ᆫᄀ ᅡ ᆯᄀ ᅧ ᅪ 5ᄀ ᅢᄋ ᅴᄌ ᅮᄉ ᆼᄇ ᅥ ᆫᅳ ᅮ ᆯ ᄋᄒ ᅬᄀ ᅱᄅ ᅩᄉ ᅡᄋ ᆼᄒ ᅭ ᅡᄂ ᆫᄎ ᅳ ᅬᄌ ᆼᄆ ᅩ ᅩᄒ ᆼᄋ ᅧ ᅵᄋ ᆮᄋ ᅥ ᅥᄌ ᆻᄀ ᅧ ᅩ, ᄉ ᅡᄒ ᅮᄇ ᆫᄑ ᅮ ᅩᄅ ᆯᄐ ᅳ ᆼᄒ ᅩ ᆫᄀ ᅡ ᆯᄀ ᅧ ᅪᄀ ᅡᄋ ᅵᄌ ᆫᄋ ᅥ ᅴᄋ ᆫ ᅧ ᅮᅪ ᄀ 이 ᄇᄉ ᆺᄒ ᅳ ᆫᅧ ᅡ ᆫ ᄇᄉ ᅮᄅ ᆯᄉ ᅳ ᆫᅢ ᅥ ᆨ ᄐᄒ ᅡᄂ ᆫᄀ ᅳ ᆯᄀ ᅧ ᅪᄅ ᆯᄃ ᅳ ᅩᄎ ᆯᄒ ᅮ ᆷᄋ ᅡ ᆯᄒ ᅳ ᆨᄋ ᅪ ᆫᄒ ᅵ ᆯᄉ ᅡ ᅮᄋ ᆻᄋ ᅵ ᆻᄃ ᅥ ᅡ..
수치
관련 문서
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We applied Principal Component Analysis(PCA) to the professional Go openings, which are the early stage in Go, to analyze them especially focused on the Go
Kim “A Study on Fault Detection Monitoring and Diagnosis System of CNG Stations Based on Principal Component Analysis(PCA)”, Journal of the Korean Institute of
In this paper, we propose a Joint Exponential Smoothing and Trend- based Principal Component Analysis (JES-TBPCA) for Anomaly Dectection focus on improving the sensitiveness
The first method is to find an optimal vector of linear combination as the regression coefficient vector of regressing for each principal component on the original data
