{"id":475994,"date":"2023-08-09T07:25:33","date_gmt":"2023-08-09T07:25:33","guid":{"rendered":""},"modified":"2023-09-05T11:11:48","modified_gmt":"2023-09-05T11:11:48","slug":"bayesian-optimization","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/vn\/wiki\/bayesian-optimization\/","title":{"rendered":"T\u1ed1i \u01b0u h\u00f3a Bayes"},"content":{"rendered":"

T\u1ed1i \u01b0u h\u00f3a Bayes l\u00e0 m\u1ed9t k\u1ef9 thu\u1eadt t\u1ed1i \u01b0u h\u00f3a m\u1ea1nh m\u1ebd \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng \u0111\u1ec3 t\u00ecm gi\u1ea3i ph\u00e1p t\u1ed1i \u01b0u cho c\u00e1c h\u00e0m m\u1ee5c ti\u00eau ph\u1ee9c t\u1ea1p v\u00e0 \u0111\u1eaft ti\u1ec1n. N\u00f3 \u0111\u1eb7c bi\u1ec7t ph\u00f9 h\u1ee3p v\u1edbi c\u00e1c t\u00ecnh hu\u1ed1ng trong \u0111\u00f3 vi\u1ec7c \u0111\u00e1nh gi\u00e1 tr\u1ef1c ti\u1ebfp h\u00e0m m\u1ee5c ti\u00eau t\u1ed1n nhi\u1ec1u th\u1eddi gian ho\u1eb7c t\u1ed1n k\u00e9m. B\u1eb1ng c\u00e1ch s\u1eed d\u1ee5ng m\u00f4 h\u00ecnh x\u00e1c su\u1ea5t \u0111\u1ec3 bi\u1ec3u di\u1ec5n h\u00e0m m\u1ee5c ti\u00eau v\u00e0 c\u1eadp nh\u1eadt l\u1eb7p \u0111i l\u1eb7p l\u1ea1i d\u1ef1a tr\u00ean d\u1eef li\u1ec7u \u0111\u01b0\u1ee3c quan s\u00e1t, t\u1ed1i \u01b0u h\u00f3a Bayes \u0111i\u1ec1u h\u01b0\u1edbng kh\u00f4ng gian t\u00ecm ki\u1ebfm m\u1ed9t c\u00e1ch hi\u1ec7u qu\u1ea3 \u0111\u1ec3 t\u00ecm ra \u0111i\u1ec3m t\u1ed1i \u01b0u.<\/p>\n

L\u1ecbch s\u1eed ngu\u1ed3n g\u1ed1c c\u1ee7a t\u1ed1i \u01b0u h\u00f3a Bayes v\u00e0 l\u1ea7n \u0111\u1ea7u ti\u00ean \u0111\u1ec1 c\u1eadp \u0111\u1ebfn n\u00f3.<\/h2>\n

Ngu\u1ed3n g\u1ed1c c\u1ee7a t\u1ed1i \u01b0u h\u00f3a Bayes c\u00f3 th\u1ec3 b\u1eaft ngu\u1ed3n t\u1eeb c\u00f4ng tr\u00ecnh c\u1ee7a John Mockus v\u00e0o nh\u1eefng n\u0103m 1970. \u00d4ng \u0111i ti\u00ean phong trong \u00fd t\u01b0\u1edfng t\u1ed1i \u01b0u h\u00f3a c\u00e1c ch\u1ee9c n\u0103ng h\u1ed9p \u0111en \u0111\u1eaft ti\u1ec1n b\u1eb1ng c\u00e1ch ch\u1ecdn tu\u1ea7n t\u1ef1 c\u00e1c \u0111i\u1ec3m m\u1eabu \u0111\u1ec3 thu th\u1eadp th\u00f4ng tin v\u1ec1 h\u00e0nh vi c\u1ee7a ch\u1ee9c n\u0103ng. Tuy nhi\u00ean, b\u1ea3n th\u00e2n thu\u1eadt ng\u1eef \u201ct\u1ed1i \u01b0u h\u00f3a Bayes\u201d \u0111\u00e3 tr\u1edf n\u00ean ph\u1ed5 bi\u1ebfn v\u00e0o nh\u1eefng n\u0103m 2000 khi c\u00e1c nh\u00e0 nghi\u00ean c\u1ee9u b\u1eaft \u0111\u1ea7u kh\u00e1m ph\u00e1 s\u1ef1 k\u1ebft h\u1ee3p gi\u1eefa m\u00f4 h\u00ecnh x\u00e1c su\u1ea5t v\u1edbi c\u00e1c k\u1ef9 thu\u1eadt t\u1ed1i \u01b0u h\u00f3a t\u1ed5ng th\u1ec3.<\/p>\n

Th\u00f4ng tin chi ti\u1ebft v\u1ec1 t\u1ed1i \u01b0u h\u00f3a Bayes. M\u1edf r\u1ed9ng ch\u1ee7 \u0111\u1ec1 T\u1ed1i \u01b0u h\u00f3a Bayesian.<\/h2>\n

T\u1ed1i \u01b0u h\u00f3a Bayes nh\u1eb1m m\u1ee5c \u0111\u00edch gi\u1ea3m thi\u1ec3u h\u00e0m m\u1ee5c ti\u00eau f<\/mi>(<\/mo>x<\/mi>)<\/mo><\/mrow>f(x)<\/annotation><\/semantics><\/math><\/span><\/span>f<\/span>(<\/span>x<\/span>)<\/span><\/span><\/span><\/span><\/span> tr\u00ean m\u1ed9t mi\u1ec1n gi\u1edbi h\u1ea1n X<\/mi><\/mrow>X<\/annotation><\/semantics><\/math><\/span><\/span>X<\/span><\/span><\/span><\/span><\/span>. Kh\u00e1i ni\u1ec7m ch\u00ednh l\u00e0 duy tr\u00ec m\u00f4 h\u00ecnh thay th\u1ebf x\u00e1c su\u1ea5t, th\u01b0\u1eddng l\u00e0 quy tr\u00ecnh Gaussian (GP), g\u1ea7n \u0111\u00fang v\u1edbi h\u00e0m m\u1ee5c ti\u00eau ch\u01b0a bi\u1ebft. GP n\u1eafm b\u1eaft s\u1ef1 ph\u00e2n ph\u1ed1i c\u1ee7a f<\/mi>(<\/mo>x<\/mi>)<\/mo><\/mrow>f(x)<\/annotation><\/semantics><\/math><\/span><\/span>f<\/span>(<\/span>x<\/span>)<\/span><\/span><\/span><\/span><\/span> v\u00e0 cung c\u1ea5p th\u01b0\u1edbc \u0111o v\u1ec1 \u0111\u1ed9 kh\u00f4ng ch\u1eafc ch\u1eafn trong d\u1ef1 \u0111o\u00e1n. T\u1ea1i m\u1ed7i l\u1ea7n l\u1eb7p, thu\u1eadt to\u00e1n \u0111\u1ec1 xu\u1ea5t \u0111i\u1ec3m ti\u1ebfp theo \u0111\u1ec3 \u0111\u00e1nh gi\u00e1 b\u1eb1ng c\u00e1ch c\u00e2n b\u1eb1ng gi\u1eefa vi\u1ec7c khai th\u00e1c (ch\u1ecdn c\u00e1c \u0111i\u1ec3m c\u00f3 gi\u00e1 tr\u1ecb h\u00e0m th\u1ea5p) v\u00e0 th\u0103m d\u00f2 (kh\u00e1m ph\u00e1 c\u00e1c v\u00f9ng kh\u00f4ng ch\u1eafc ch\u1eafn).<\/p>\n

C\u00e1c b\u01b0\u1edbc li\u00ean quan \u0111\u1ebfn t\u1ed1i \u01b0u h\u00f3a Bayes nh\u01b0 sau:<\/p>\n

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  1. \n

    Ch\u1ee9c n\u0103ng mua l\u1ea1i<\/strong>: Ch\u1ee9c n\u0103ng thu th\u1eadp h\u01b0\u1edbng d\u1eabn t\u00ecm ki\u1ebfm b\u1eb1ng c\u00e1ch ch\u1ecdn \u0111i\u1ec3m ti\u1ebfp theo \u0111\u1ec3 \u0111\u00e1nh gi\u00e1 d\u1ef1a tr\u00ean c\u00e1c d\u1ef1 \u0111o\u00e1n v\u00e0 \u01b0\u1edbc t\u00ednh \u0111\u1ed9 kh\u00f4ng ch\u1eafc ch\u1eafn c\u1ee7a m\u00f4 h\u00ecnh thay th\u1ebf. C\u00e1c h\u00e0m thu th\u1eadp ph\u1ed5 bi\u1ebfn bao g\u1ed3m X\u00e1c su\u1ea5t C\u1ea3i thi\u1ec7n (PI), C\u1ea3i thi\u1ec7n D\u1ef1 ki\u1ebfn (EI) v\u00e0 Gi\u1edbi h\u1ea1n Ni\u1ec1m tin Tr\u00ean (UCB).<\/p>\n<\/li>\n

  2. \n

    M\u00f4 h\u00ecnh thay th\u1ebf<\/strong>: Quy tr\u00ecnh Gaussian l\u00e0 m\u00f4 h\u00ecnh thay th\u1ebf ph\u1ed5 bi\u1ebfn \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng trong t\u1ed1i \u01b0u h\u00f3a Bayes. N\u00f3 cho ph\u00e9p \u01b0\u1edbc t\u00ednh hi\u1ec7u qu\u1ea3 h\u00e0m m\u1ee5c ti\u00eau v\u00e0 \u0111\u1ed9 kh\u00f4ng \u0111\u1ea3m b\u1ea3o c\u1ee7a n\u00f3. C\u00e1c m\u00f4 h\u00ecnh thay th\u1ebf kh\u00e1c nh\u01b0 R\u1eebng ng\u1eabu nhi\u00ean ho\u1eb7c M\u1ea1ng th\u1ea7n kinh Bayesian c\u0169ng c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng t\u00f9y theo v\u1ea5n \u0111\u1ec1.<\/p>\n<\/li>\n

  3. \n

    T\u1ed1i \u01b0u h\u00f3a<\/strong>: Sau khi x\u00e1c \u0111\u1ecbnh \u0111\u01b0\u1ee3c h\u00e0m thu th\u1eadp, c\u00e1c k\u1ef9 thu\u1eadt t\u1ed1i \u01b0u h\u00f3a nh\u01b0 L-BFGS, thu\u1eadt to\u00e1n di truy\u1ec1n ho\u1eb7c ch\u00ednh t\u1ed1i \u01b0u h\u00f3a Bayes (v\u1edbi m\u00f4 h\u00ecnh thay th\u1ebf c\u00f3 chi\u1ec1u th\u1ea5p h\u01a1n) \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng \u0111\u1ec3 t\u00ecm ra \u0111i\u1ec3m t\u1ed1i \u01b0u.<\/p>\n<\/li>\n

  4. \n

    C\u1eadp nh\u1eadt ng\u01b0\u1eddi thay th\u1ebf<\/strong>: Sau khi \u0111\u00e1nh gi\u00e1 h\u00e0m m\u1ee5c ti\u00eau t\u1ea1i \u0111i\u1ec3m g\u1ee3i \u00fd, m\u00f4 h\u00ecnh thay th\u1ebf \u0111\u01b0\u1ee3c c\u1eadp nh\u1eadt \u0111\u1ec3 k\u1ebft h\u1ee3p quan s\u00e1t m\u1edbi. Qu\u00e1 tr\u00ecnh l\u1eb7p l\u1ea1i n\u00e0y ti\u1ebfp t\u1ee5c cho \u0111\u1ebfn khi \u0111\u1ea1t \u0111\u01b0\u1ee3c s\u1ef1 h\u1ed9i t\u1ee5 ho\u1eb7c ti\u00eau ch\u00ed d\u1eebng \u0111\u01b0\u1ee3c x\u00e1c \u0111\u1ecbnh tr\u01b0\u1edbc.<\/p>\n<\/li>\n<\/ol>\n

    C\u1ea5u tr\u00fac b\u00ean trong c\u1ee7a t\u1ed1i \u01b0u h\u00f3a Bayes. C\u00e1ch t\u1ed1i \u01b0u h\u00f3a Bayes ho\u1ea1t \u0111\u1ed9ng.<\/h2>\n

    T\u1ed1i \u01b0u h\u00f3a Bayes bao g\u1ed3m hai th\u00e0nh ph\u1ea7n ch\u00ednh: m\u00f4 h\u00ecnh thay th\u1ebf v\u00e0 h\u00e0m thu th\u1eadp.<\/p>\n

    M\u00f4 h\u00ecnh thay th\u1ebf<\/h3>\n

    M\u00f4 h\u00ecnh thay th\u1ebf x\u1ea5p x\u1ec9 h\u00e0m m\u1ee5c ti\u00eau ch\u01b0a bi\u1ebft d\u1ef1a tr\u00ean d\u1eef li\u1ec7u \u0111\u01b0\u1ee3c quan s\u00e1t. Quy tr\u00ecnh Gaussian (GP) th\u01b0\u1eddng \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng l\u00e0m m\u00f4 h\u00ecnh thay th\u1ebf do t\u00ednh linh ho\u1ea1t v\u00e0 kh\u1ea3 n\u0103ng n\u1eafm b\u1eaft \u0111\u01b0\u1ee3c s\u1ef1 kh\u00f4ng ch\u1eafc ch\u1eafn c\u1ee7a n\u00f3. GP x\u00e1c \u0111\u1ecbnh ph\u00e2n ph\u1ed1i tr\u01b0\u1edbc tr\u00ean c\u00e1c h\u00e0m v\u00e0 \u0111\u01b0\u1ee3c c\u1eadp nh\u1eadt d\u1eef li\u1ec7u m\u1edbi \u0111\u1ec3 c\u00f3 \u0111\u01b0\u1ee3c ph\u00e2n ph\u1ed1i sau, \u0111\u1ea1i di\u1ec7n cho h\u00e0m c\u00f3 th\u1ec3 x\u1ea3y ra nh\u1ea5t d\u1ef1a tr\u00ean d\u1eef li\u1ec7u \u0111\u01b0\u1ee3c quan s\u00e1t.<\/p>\n

    GP \u0111\u01b0\u1ee3c \u0111\u1eb7c tr\u01b0ng b\u1edfi h\u00e0m trung b\u00ecnh v\u00e0 h\u00e0m hi\u1ec7p ph\u01b0\u01a1ng sai (kernel). H\u00e0m trung b\u00ecnh \u01b0\u1edbc t\u00ednh gi\u00e1 tr\u1ecb k\u1ef3 v\u1ecdng c\u1ee7a h\u00e0m m\u1ee5c ti\u00eau v\u00e0 h\u00e0m hi\u1ec7p ph\u01b0\u01a1ng sai \u0111o l\u01b0\u1eddng m\u1ee9c \u0111\u1ed9 t\u01b0\u01a1ng t\u1ef1 gi\u1eefa c\u00e1c gi\u00e1 tr\u1ecb h\u00e0m t\u1ea1i c\u00e1c \u0111i\u1ec3m kh\u00e1c nhau. Vi\u1ec7c l\u1ef1a ch\u1ecdn h\u1ea1t nh\u00e2n ph\u1ee5 thu\u1ed9c v\u00e0o \u0111\u1eb7c \u0111i\u1ec3m c\u1ee7a h\u00e0m m\u1ee5c ti\u00eau, ch\u1eb3ng h\u1ea1n nh\u01b0 \u0111\u1ed9 tr\u01a1n ho\u1eb7c t\u00ednh tu\u1ea7n ho\u00e0n.<\/p>\n

    Ch\u1ee9c n\u0103ng mua l\u1ea1i<\/h3>\n

    Ch\u1ee9c n\u0103ng thu th\u1eadp \u0111\u00f3ng vai tr\u00f2 quan tr\u1ecdng trong vi\u1ec7c h\u01b0\u1edbng d\u1eabn qu\u00e1 tr\u00ecnh t\u1ed1i \u01b0u h\u00f3a b\u1eb1ng c\u00e1ch c\u00e2n b\u1eb1ng gi\u1eefa th\u0103m d\u00f2 v\u00e0 khai th\u00e1c. N\u00f3 \u0111\u1ecbnh l\u01b0\u1ee3ng ti\u1ec1m n\u0103ng c\u1ee7a m\u1ed9t \u0111i\u1ec3m l\u00e0 t\u1ed1i \u01b0u to\u00e0n c\u1ea7u. M\u1ed9t s\u1ed1 ch\u1ee9c n\u0103ng thu th\u1eadp ph\u1ed5 bi\u1ebfn th\u01b0\u1eddng \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng:<\/p>\n

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    1. \n

      X\u00e1c su\u1ea5t c\u1ea3i thi\u1ec7n (PI)<\/strong>: H\u00e0m n\u00e0y ch\u1ecdn \u0111i\u1ec3m c\u00f3 x\u00e1c su\u1ea5t c\u1ea3i thi\u1ec7n cao nh\u1ea5t d\u1ef1a tr\u00ean gi\u00e1 tr\u1ecb t\u1ed1t nh\u1ea5t hi\u1ec7n t\u1ea1i.<\/p>\n<\/li>\n

    2. \n

      C\u1ea3i thi\u1ec7n d\u1ef1 ki\u1ebfn (EI)<\/strong>: N\u00f3 xem x\u00e9t c\u1ea3 x\u00e1c su\u1ea5t c\u1ea3i thi\u1ec7n v\u00e0 c\u1ea3i thi\u1ec7n d\u1ef1 ki\u1ebfn v\u1ec1 gi\u00e1 tr\u1ecb h\u00e0m.<\/p>\n<\/li>\n

    3. \n

      Gi\u1edbi h\u1ea1n ni\u1ec1m tin tr\u00ean (UCB)<\/strong>: UCB c\u00e2n b\u1eb1ng vi\u1ec7c th\u0103m d\u00f2 v\u00e0 khai th\u00e1c b\u1eb1ng c\u00e1ch s\u1eed d\u1ee5ng tham s\u1ed1 c\u00e2n b\u1eb1ng ki\u1ec3m so\u00e1t s\u1ef1 c\u00e2n b\u1eb1ng gi\u1eefa \u0111\u1ed9 kh\u00f4ng \u0111\u1ea3m b\u1ea3o v\u00e0 gi\u00e1 tr\u1ecb h\u00e0m d\u1ef1 \u0111o\u00e1n.<\/p>\n<\/li>\n<\/ol>\n

      Ch\u1ee9c n\u0103ng thu th\u1eadp h\u01b0\u1edbng d\u1eabn l\u1ef1a ch\u1ecdn \u0111i\u1ec3m ti\u1ebfp theo \u0111\u1ec3 \u0111\u00e1nh gi\u00e1 v\u00e0 qu\u00e1 tr\u00ecnh n\u00e0y ti\u1ebfp t\u1ee5c l\u1eb7p \u0111i l\u1eb7p l\u1ea1i cho \u0111\u1ebfn khi t\u00ecm th\u1ea5y gi\u1ea3i ph\u00e1p t\u1ed1i \u01b0u.<\/p>\n

      Ph\u00e2n t\u00edch c\u00e1c t\u00ednh n\u0103ng ch\u00ednh c\u1ee7a t\u1ed1i \u01b0u h\u00f3a Bayes.<\/h2>\n

      T\u1ed1i \u01b0u h\u00f3a Bayesian cung c\u1ea5p m\u1ed9t s\u1ed1 t\u00ednh n\u0103ng ch\u00ednh khi\u1ebfn n\u00f3 tr\u1edf n\u00ean h\u1ea5p d\u1eabn \u0111\u1ed1i v\u1edbi c\u00e1c nhi\u1ec7m v\u1ee5 t\u1ed1i \u01b0u h\u00f3a kh\u00e1c nhau:<\/p>\n

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      1. \n

        Hi\u1ec7u qu\u1ea3 m\u1eabu<\/strong>: T\u1ed1i \u01b0u h\u00f3a Bayes c\u00f3 th\u1ec3 t\u00ecm ra gi\u1ea3i ph\u00e1p t\u1ed1i \u01b0u m\u1ed9t c\u00e1ch hi\u1ec7u qu\u1ea3 v\u1edbi t\u01b0\u01a1ng \u0111\u1ed1i \u00edt \u0111\u00e1nh gi\u00e1 v\u1ec1 h\u00e0m m\u1ee5c ti\u00eau. \u0110i\u1ec1u n\u00e0y \u0111\u1eb7c bi\u1ec7t c\u00f3 gi\u00e1 tr\u1ecb khi vi\u1ec7c \u0111\u00e1nh gi\u00e1 ch\u1ee9c n\u0103ng t\u1ed1n nhi\u1ec1u th\u1eddi gian ho\u1eb7c t\u1ed1n k\u00e9m.<\/p>\n<\/li>\n

      2. \n

        T\u1ed1i \u01b0u h\u00f3a to\u00e0n c\u1ea7u<\/strong>: Kh\u00f4ng gi\u1ed1ng nh\u01b0 c\u00e1c ph\u01b0\u01a1ng ph\u00e1p d\u1ef1a tr\u00ean gradient, t\u1ed1i \u01b0u h\u00f3a Bayes l\u00e0 m\u1ed9t k\u1ef9 thu\u1eadt t\u1ed1i \u01b0u h\u00f3a to\u00e0n c\u1ee5c. N\u00f3 kh\u00e1m ph\u00e1 kh\u00f4ng gian t\u00ecm ki\u1ebfm m\u1ed9t c\u00e1ch hi\u1ec7u qu\u1ea3 \u0111\u1ec3 x\u00e1c \u0111\u1ecbnh v\u1ecb tr\u00ed t\u1ed1i \u01b0u to\u00e0n c\u1ee5c thay v\u00ec b\u1ecb m\u1eafc k\u1eb9t trong t\u1ed1i \u01b0u c\u1ee5c b\u1ed9.<\/p>\n<\/li>\n

      3. \n

        Bi\u1ec3u di\u1ec5n x\u00e1c su\u1ea5t<\/strong>: Vi\u1ec7c bi\u1ec3u di\u1ec5n x\u00e1c su\u1ea5t c\u1ee7a h\u00e0m m\u1ee5c ti\u00eau b\u1eb1ng Quy tr\u00ecnh Gaussian cho ph\u00e9p ch\u00fang ta \u0111\u1ecbnh l\u01b0\u1ee3ng \u0111\u1ed9 kh\u00f4ng \u0111\u1ea3m b\u1ea3o trong c\u00e1c d\u1ef1 \u0111o\u00e1n. \u0110i\u1ec1u n\u00e0y \u0111\u1eb7c bi\u1ec7t c\u00f3 gi\u00e1 tr\u1ecb khi x\u1eed l\u00fd c\u00e1c h\u00e0m m\u1ee5c ti\u00eau nhi\u1ec5u ho\u1eb7c kh\u00f4ng ch\u1eafc ch\u1eafn.<\/p>\n<\/li>\n

      4. \n

        R\u00e0ng bu\u1ed9c do ng\u01b0\u1eddi d\u00f9ng x\u00e1c \u0111\u1ecbnh<\/strong>: T\u1ed1i \u01b0u h\u00f3a Bayes d\u1ec5 d\u00e0ng \u0111\u00e1p \u1ee9ng c\u00e1c r\u00e0ng bu\u1ed9c do ng\u01b0\u1eddi d\u00f9ng x\u00e1c \u0111\u1ecbnh, l\u00e0m cho n\u00f3 ph\u00f9 h\u1ee3p v\u1edbi c\u00e1c v\u1ea5n \u0111\u1ec1 t\u1ed1i \u01b0u h\u00f3a b\u1ecb r\u00e0ng bu\u1ed9c.<\/p>\n<\/li>\n

      5. \n

        Kh\u00e1m ph\u00e1 th\u00edch \u1ee9ng<\/strong>: Ch\u1ee9c n\u0103ng thu th\u1eadp cho ph\u00e9p kh\u00e1m ph\u00e1 th\u00edch \u1ee9ng, cho ph\u00e9p thu\u1eadt to\u00e1n t\u1eadp trung v\u00e0o c\u00e1c khu v\u1ef1c c\u00f3 tri\u1ec3n v\u1ecdng trong khi v\u1eabn kh\u00e1m ph\u00e1 c\u00e1c khu v\u1ef1c ch\u01b0a ch\u1eafc ch\u1eafn.<\/p>\n<\/li>\n<\/ol>\n

        C\u00e1c lo\u1ea1i t\u1ed1i \u01b0u h\u00f3a Bayes<\/h2>\n

        T\u1ed1i \u01b0u h\u00f3a Bayes c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c ph\u00e2n lo\u1ea1i d\u1ef1a tr\u00ean nhi\u1ec1u y\u1ebfu t\u1ed1 kh\u00e1c nhau, ch\u1eb3ng h\u1ea1n nh\u01b0 m\u00f4 h\u00ecnh thay th\u1ebf \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng ho\u1eb7c lo\u1ea1i v\u1ea5n \u0111\u1ec1 t\u1ed1i \u01b0u h\u00f3a.<\/p>\n

        D\u1ef1a tr\u00ean m\u00f4 h\u00ecnh thay th\u1ebf:<\/h3>\n
          \n
        1. \n

          T\u1ed1i \u01b0u h\u00f3a Bayesian d\u1ef1a tr\u00ean quy tr\u00ecnh Gaussian<\/strong>: \u0110\u00e2y l\u00e0 lo\u1ea1i ph\u1ed5 bi\u1ebfn nh\u1ea5t, s\u1eed d\u1ee5ng Quy tr\u00ecnh Gaussian l\u00e0m m\u00f4 h\u00ecnh thay th\u1ebf \u0111\u1ec3 n\u1eafm b\u1eaft \u0111\u1ed9 kh\u00f4ng \u0111\u1ea3m b\u1ea3o c\u1ee7a h\u00e0m m\u1ee5c ti\u00eau.<\/p>\n<\/li>\n

        2. \n

          T\u1ed1i \u01b0u h\u00f3a Bayes d\u1ef1a tr\u00ean r\u1eebng ng\u1eabu nhi\u00ean<\/strong>: N\u00f3 thay th\u1ebf Quy tr\u00ecnh Gaussian b\u1eb1ng R\u1eebng ng\u1eabu nhi\u00ean \u0111\u1ec3 m\u00f4 h\u00ecnh h\u00f3a h\u00e0m m\u1ee5c ti\u00eau v\u00e0 \u0111\u1ed9 kh\u00f4ng \u0111\u1ea3m b\u1ea3o c\u1ee7a n\u00f3.<\/p>\n<\/li>\n

        3. \n

          T\u1ed1i \u01b0u h\u00f3a Bayesian d\u1ef1a tr\u00ean m\u1ea1ng th\u1ea7n kinh Bayesian<\/strong>: Bi\u1ebfn th\u1ec3 n\u00e0y s\u1eed d\u1ee5ng M\u1ea1ng th\u1ea7n kinh Bayesian l\u00e0m m\u00f4 h\u00ecnh thay th\u1ebf, l\u00e0 c\u00e1c m\u1ea1ng th\u1ea7n kinh c\u00f3 tr\u1ecdng s\u1ed1 \u01b0u ti\u00ean Bayesian.<\/p>\n<\/li>\n<\/ol>\n

          D\u1ef1a tr\u00ean v\u1ea5n \u0111\u1ec1 t\u1ed1i \u01b0u h\u00f3a:<\/h3>\n
            \n
          1. \n

            T\u1ed1i \u01b0u h\u00f3a Bayes m\u1ee5c ti\u00eau \u0111\u01a1n<\/strong>: \u0110\u01b0\u1ee3c s\u1eed d\u1ee5ng \u0111\u1ec3 t\u1ed1i \u01b0u h\u00f3a m\u1ed9t h\u00e0m m\u1ee5c ti\u00eau duy nh\u1ea5t.<\/p>\n<\/li>\n

          2. \n

            T\u1ed1i \u01b0u h\u00f3a Bayes \u0111a m\u1ee5c ti\u00eau<\/strong>: \u0110\u01b0\u1ee3c thi\u1ebft k\u1ebf cho c\u00e1c b\u00e0i to\u00e1n c\u00f3 nhi\u1ec1u m\u1ee5c ti\u00eau xung \u0111\u1ed9t nhau, t\u00ecm ki\u1ebfm m\u1ed9t t\u1eadp h\u1ee3p c\u00e1c gi\u1ea3i ph\u00e1p t\u1ed1i \u01b0u Pareto.<\/p>\n<\/li>\n<\/ol>\n

            C\u00e1c c\u00e1ch s\u1eed d\u1ee5ng t\u1ed1i \u01b0u h\u00f3a Bayes, c\u00e1c v\u1ea5n \u0111\u1ec1 v\u00e0 gi\u1ea3i ph\u00e1p li\u00ean quan \u0111\u1ebfn vi\u1ec7c s\u1eed d\u1ee5ng.<\/h2>\n

            T\u1ed1i \u01b0u h\u00f3a Bayes t\u00ecm th\u1ea5y c\u00e1c \u1ee9ng d\u1ee5ng trong nhi\u1ec1u l\u0129nh v\u1ef1c kh\u00e1c nhau do t\u00ednh linh ho\u1ea1t v\u00e0 hi\u1ec7u qu\u1ea3 c\u1ee7a n\u00f3. M\u1ed9t s\u1ed1 tr\u01b0\u1eddng h\u1ee3p s\u1eed d\u1ee5ng ph\u1ed5 bi\u1ebfn bao g\u1ed3m:<\/p>\n

              \n
            1. \n

              \u0110i\u1ec1u ch\u1ec9nh si\u00eau tham s\u1ed1<\/strong>: T\u1ed1i \u01b0u h\u00f3a Bayes \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng r\u1ed9ng r\u00e3i \u0111\u1ec3 t\u1ed1i \u01b0u h\u00f3a si\u00eau tham s\u1ed1 c\u1ee7a c\u00e1c m\u00f4 h\u00ecnh h\u1ecdc m\u00e1y, n\u00e2ng cao hi\u1ec7u su\u1ea5t v\u00e0 t\u00ednh kh\u00e1i qu\u00e1t c\u1ee7a ch\u00fang.<\/p>\n<\/li>\n

            2. \n

              Ng\u01b0\u1eddi m\u00e1y<\/strong>: Trong ch\u1ebf t\u1ea1o robot, t\u1ed1i \u01b0u h\u00f3a Bayes gi\u00fap t\u1ed1i \u01b0u h\u00f3a c\u00e1c tham s\u1ed1 v\u00e0 ch\u00ednh s\u00e1ch ki\u1ec3m so\u00e1t cho c\u00e1c t\u00e1c v\u1ee5 nh\u01b0 n\u1eafm b\u1eaft, l\u1eadp k\u1ebf ho\u1ea1ch \u0111\u01b0\u1eddng \u0111i v\u00e0 thao t\u00e1c \u0111\u1ed1i t\u01b0\u1ee3ng.<\/p>\n<\/li>\n

            3. \n

              Thi\u1ebft k\u1ebf th\u1eed nghi\u1ec7m<\/strong>: T\u1ed1i \u01b0u h\u00f3a Bayes h\u1ed7 tr\u1ee3 vi\u1ec7c thi\u1ebft k\u1ebf c\u00e1c th\u00ed nghi\u1ec7m b\u1eb1ng c\u00e1ch l\u1ef1a ch\u1ecdn hi\u1ec7u qu\u1ea3 c\u00e1c \u0111i\u1ec3m m\u1eabu trong kh\u00f4ng gian tham s\u1ed1 nhi\u1ec1u chi\u1ec1u.<\/p>\n<\/li>\n

            4. \n

              \u0110i\u1ec1u ch\u1ec9nh m\u00f4 ph\u1ecfng<\/strong>: N\u00f3 \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng \u0111\u1ec3 t\u1ed1i \u01b0u h\u00f3a c\u00e1c m\u00f4 ph\u1ecfng v\u00e0 m\u00f4 h\u00ecnh t\u00ednh to\u00e1n ph\u1ee9c t\u1ea1p trong c\u00e1c l\u0129nh v\u1ef1c khoa h\u1ecdc v\u00e0 k\u1ef9 thu\u1eadt.<\/p>\n<\/li>\n

            5. \n

              Nghi\u00ean c\u1ee9u ch\u1ebf t\u1ea1o thu\u1ed1c<\/strong>: T\u1ed1i \u01b0u h\u00f3a Bayes c\u00f3 th\u1ec3 t\u0103ng t\u1ed1c qu\u00e1 tr\u00ecnh ph\u00e1t hi\u1ec7n thu\u1ed1c b\u1eb1ng c\u00e1ch s\u00e0ng l\u1ecdc hi\u1ec7u qu\u1ea3 c\u00e1c h\u1ee3p ch\u1ea5t thu\u1ed1c ti\u1ec1m n\u0103ng.<\/p>\n<\/li>\n<\/ol>\n

              M\u1eb7c d\u00f9 t\u1ed1i \u01b0u h\u00f3a Bayesian mang l\u1ea1i nhi\u1ec1u l\u1ee3i \u00edch nh\u01b0ng n\u00f3 c\u0169ng ph\u1ea3i \u0111\u1ed1i m\u1eb7t v\u1edbi nh\u1eefng th\u00e1ch th\u1ee9c:<\/p>\n

                \n
              1. \n

                T\u1ed1i \u01b0u h\u00f3a chi\u1ec1u cao<\/strong>: T\u1ed1i \u01b0u h\u00f3a Bayes tr\u1edf n\u00ean t\u1ed1n k\u00e9m v\u1ec1 m\u1eb7t t\u00ednh to\u00e1n trong kh\u00f4ng gian nhi\u1ec1u chi\u1ec1u do h\u1ea1n ch\u1ebf v\u1ec1 chi\u1ec1u.<\/p>\n<\/li>\n

              2. \n

                \u0110\u00e1nh gi\u00e1 t\u1ed1n k\u00e9m<\/strong>: N\u1ebfu vi\u1ec7c \u0111\u00e1nh gi\u00e1 h\u00e0m m\u1ee5c ti\u00eau r\u1ea5t t\u1ed1n k\u00e9m ho\u1eb7c t\u1ed1n th\u1eddi gian th\u00ec qu\u00e1 tr\u00ecnh t\u1ed1i \u01b0u h\u00f3a c\u00f3 th\u1ec3 tr\u1edf n\u00ean kh\u00f4ng th\u1ef1c t\u1ebf.<\/p>\n<\/li>\n

              3. \n

                H\u1ed9i t\u1ee5 t\u1edbi Optima \u0111\u1ecba ph\u01b0\u01a1ng<\/strong>: M\u1eb7c d\u00f9 t\u1ed1i \u01b0u h\u00f3a Bayes \u0111\u01b0\u1ee3c thi\u1ebft k\u1ebf \u0111\u1ec3 t\u1ed1i \u01b0u h\u00f3a to\u00e0n c\u1ee5c, n\u00f3 v\u1eabn c\u00f3 th\u1ec3 h\u1ed9i t\u1ee5 v\u1ec1 t\u1ed1i \u01b0u c\u1ee5c b\u1ed9 n\u1ebfu s\u1ef1 c\u00e2n b\u1eb1ng th\u0103m d\u00f2-khai th\u00e1c kh\u00f4ng \u0111\u01b0\u1ee3c thi\u1ebft l\u1eadp ph\u00f9 h\u1ee3p.<\/p>\n<\/li>\n<\/ol>\n

                \u0110\u1ec3 v\u01b0\u1ee3t qua nh\u1eefng th\u00e1ch th\u1ee9c n\u00e0y, nh\u1eefng ng\u01b0\u1eddi th\u1ef1c h\u00e0nh th\u01b0\u1eddng s\u1eed d\u1ee5ng c\u00e1c k\u1ef9 thu\u1eadt nh\u01b0 gi\u1ea3m k\u00edch th\u01b0\u1edbc, song song h\u00f3a ho\u1eb7c thi\u1ebft k\u1ebf ch\u1ee9c n\u0103ng thu th\u1eadp th\u00f4ng minh.<\/p>\n

                C\u00e1c \u0111\u1eb7c \u0111i\u1ec3m ch\u00ednh v\u00e0 c\u00e1c so s\u00e1nh kh\u00e1c v\u1edbi c\u00e1c thu\u1eadt ng\u1eef t\u01b0\u01a1ng t\u1ef1 d\u01b0\u1edbi d\u1ea1ng b\u1ea3ng v\u00e0 danh s\u00e1ch.<\/h2>\n\n\n\n\n\n\n\n\n\n\n
                \u0111\u1eb7c tr\u01b0ng<\/th>\nT\u1ed1i \u01b0u h\u00f3a Bayes<\/th>\nT\u00ecm ki\u1ebfm l\u01b0\u1edbi<\/th>\nT\u00ecm ki\u1ebfm ng\u1eabu nhi\u00ean<\/th>\nThu\u1eadt to\u00e1n ti\u1ebfn h\u00f3a<\/th>\n<\/tr>\n<\/thead>\n
                T\u1ed1i \u01b0u h\u00f3a to\u00e0n c\u1ea7u<\/td>\n\u0110\u00fang<\/td>\nKH\u00d4NG<\/td>\nKH\u00d4NG<\/td>\n\u0110\u00fang<\/td>\n<\/tr>\n
                Hi\u1ec7u qu\u1ea3 m\u1eabu<\/td>\nCao<\/td>\nTh\u1ea5p<\/td>\nTh\u1ea5p<\/td>\nTrung b\u00ecnh<\/td>\n<\/tr>\n
                \u0110\u00e1nh gi\u00e1 \u0111\u1eaft gi\u00e1<\/td>\nTh\u00edch h\u1ee3p<\/td>\nTh\u00edch h\u1ee3p<\/td>\nTh\u00edch h\u1ee3p<\/td>\nTh\u00edch h\u1ee3p<\/td>\n<\/tr>\n
                Bi\u1ec3u di\u1ec5n x\u00e1c su\u1ea5t<\/td>\n\u0110\u00fang<\/td>\nKH\u00d4NG<\/td>\nKH\u00d4NG<\/td>\nKH\u00d4NG<\/td>\n<\/tr>\n
                Kh\u00e1m ph\u00e1 th\u00edch \u1ee9ng<\/td>\n\u0110\u00fang<\/td>\nKH\u00d4NG<\/td>\n\u0110\u00fang<\/td>\n\u0110\u00fang<\/td>\n<\/tr>\n
                X\u1eed l\u00fd c\u00e1c r\u00e0ng bu\u1ed9c<\/td>\n\u0110\u00fang<\/td>\nKH\u00d4NG<\/td>\nKH\u00d4NG<\/td>\n\u0110\u00fang<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

                C\u00e1c quan \u0111i\u1ec3m v\u00e0 c\u00f4ng ngh\u1ec7 c\u1ee7a t\u01b0\u01a1ng lai li\u00ean quan \u0111\u1ebfn t\u1ed1i \u01b0u h\u00f3a Bayes.<\/h2>\n

                T\u01b0\u01a1ng lai c\u1ee7a t\u1ed1i \u01b0u h\u00f3a Bayes c\u00f3 v\u1ebb \u0111\u1ea7y h\u1ee9a h\u1eb9n v\u1edbi m\u1ed9t s\u1ed1 ti\u1ebfn b\u1ed9 v\u00e0 c\u00f4ng ngh\u1ec7 ti\u1ec1m n\u0103ng s\u1eafp ra m\u1eaft:<\/p>\n

                  \n
                1. \n

                  Kh\u1ea3 n\u0103ng m\u1edf r\u1ed9ng<\/strong>: C\u00e1c nh\u00e0 nghi\u00ean c\u1ee9u \u0111ang t\u00edch c\u1ef1c l\u00e0m vi\u1ec7c \u0111\u1ec3 m\u1edf r\u1ed9ng c\u00e1c k\u1ef9 thu\u1eadt t\u1ed1i \u01b0u h\u00f3a Bayesian \u0111\u1ec3 x\u1eed l\u00fd c\u00e1c v\u1ea5n \u0111\u1ec1 c\u00f3 chi\u1ec1u cao v\u00e0 t\u00ednh to\u00e1n ph\u1ee9c t\u1ea1p m\u1ed9t c\u00e1ch hi\u1ec7u qu\u1ea3 h\u01a1n.<\/p>\n<\/li>\n

                2. \n

                  Song song h\u00f3a<\/strong>: Nh\u1eefng ti\u1ebfn b\u1ed9 h\u01a1n n\u1eefa trong t\u00ednh to\u00e1n song song c\u00f3 th\u1ec3 t\u0103ng t\u1ed1c \u0111\u00e1ng k\u1ec3 vi\u1ec7c t\u1ed1i \u01b0u h\u00f3a Bayes b\u1eb1ng c\u00e1ch \u0111\u00e1nh gi\u00e1 nhi\u1ec1u \u0111i\u1ec3m c\u00f9ng m\u1ed9t l\u00fac.<\/p>\n<\/li>\n

                3. \n

                  Chuy\u1ec3n ti\u1ebfp h\u1ecdc t\u1eadp<\/strong>: C\u00e1c k\u1ef9 thu\u1eadt t\u1eeb h\u1ecdc chuy\u1ec3n giao v\u00e0 si\u00eau h\u1ecdc c\u00f3 th\u1ec3 n\u00e2ng cao hi\u1ec7u qu\u1ea3 t\u1ed1i \u01b0u h\u00f3a Bayes b\u1eb1ng c\u00e1ch t\u1eadn d\u1ee5ng ki\u1ebfn th\u1ee9c t\u1eeb c\u00e1c nhi\u1ec7m v\u1ee5 t\u1ed1i \u01b0u h\u00f3a tr\u01b0\u1edbc \u0111\u00f3.<\/p>\n<\/li>\n

                4. \n

                  M\u1ea1ng th\u1ea7n kinh Bayes<\/strong>: M\u1ea1ng th\u1ea7n kinh Bayesian cho th\u1ea5y h\u1ee9a h\u1eb9n trong vi\u1ec7c c\u1ea3i thi\u1ec7n kh\u1ea3 n\u0103ng l\u1eadp m\u00f4 h\u00ecnh c\u1ee7a c\u00e1c m\u00f4 h\u00ecnh thay th\u1ebf, d\u1eabn \u0111\u1ebfn \u01b0\u1edbc t\u00ednh \u0111\u1ed9 kh\u00f4ng \u0111\u1ea3m b\u1ea3o t\u1ed1t h\u01a1n.<\/p>\n<\/li>\n

                5. \n

                  H\u1ecdc m\u00e1y t\u1ef1 \u0111\u1ed9ng<\/strong>: T\u1ed1i \u01b0u h\u00f3a Bayes d\u1ef1 ki\u1ebfn s\u1ebd \u0111\u00f3ng m\u1ed9t vai tr\u00f2 quan tr\u1ecdng trong vi\u1ec7c t\u1ef1 \u0111\u1ed9ng h\u00f3a quy tr\u00ecnh h\u1ecdc m\u00e1y, t\u1ed1i \u01b0u h\u00f3a quy tr\u00ecnh v\u00e0 t\u1ef1 \u0111\u1ed9ng \u0111i\u1ec1u ch\u1ec9nh si\u00eau tham s\u1ed1.<\/p>\n<\/li>\n

                6. \n

                  H\u1ecdc t\u0103ng c\u01b0\u1eddng<\/strong>: Vi\u1ec7c t\u00edch h\u1ee3p t\u1ed1i \u01b0u h\u00f3a Bayes v\u1edbi c\u00e1c thu\u1eadt to\u00e1n h\u1ecdc t\u0103ng c\u01b0\u1eddng c\u00f3 th\u1ec3 d\u1eabn \u0111\u1ebfn vi\u1ec7c kh\u00e1m ph\u00e1 m\u1eabu hi\u1ec7u qu\u1ea3 h\u01a1n v\u00e0 hi\u1ec7u qu\u1ea3 h\u01a1n trong c\u00e1c nhi\u1ec7m v\u1ee5 RL.<\/p>\n<\/li>\n<\/ol>\n

                  C\u00e1ch s\u1eed d\u1ee5ng m\u00e1y ch\u1ee7 proxy ho\u1eb7c li\u00ean k\u1ebft v\u1edbi t\u1ed1i \u01b0u h\u00f3a Bayesian.<\/h2>\n

                  M\u00e1y ch\u1ee7 proxy c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c li\u00ean k\u1ebft ch\u1eb7t ch\u1ebd v\u1edbi t\u1ed1i \u01b0u h\u00f3a Bayes theo nhi\u1ec1u c\u00e1ch kh\u00e1c nhau:<\/p>\n

                    \n
                  1. \n

                    T\u1ed1i \u01b0u h\u00f3a Bayes ph\u00e2n t\u00e1n<\/strong>: Khi s\u1eed d\u1ee5ng nhi\u1ec1u m\u00e1y ch\u1ee7 proxy tr\u1ea3i r\u1ed9ng tr\u00ean c\u00e1c v\u1ecb tr\u00ed \u0111\u1ecba l\u00fd kh\u00e1c nhau, t\u1ed1i \u01b0u h\u00f3a Bayesian c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c th\u1ef1c hi\u1ec7n song song, d\u1eabn \u0111\u1ebfn kh\u1ea3 n\u0103ng h\u1ed9i t\u1ee5 nhanh h\u01a1n v\u00e0 kh\u00e1m ph\u00e1 kh\u00f4ng gian t\u00ecm ki\u1ebfm t\u1ed1t h\u01a1n.<\/p>\n<\/li>\n

                  2. \n

                    Quy\u1ec1n ri\u00eang t\u01b0 v\u00e0 b\u1ea3o m\u1eadt<\/strong>: Trong tr\u01b0\u1eddng h\u1ee3p \u0111\u00e1nh gi\u00e1 ch\u1ee9c n\u0103ng kh\u00e1ch quan li\u00ean quan \u0111\u1ebfn d\u1eef li\u1ec7u nh\u1ea1y c\u1ea3m ho\u1eb7c b\u00ed m\u1eadt, m\u00e1y ch\u1ee7 proxy c\u00f3 th\u1ec3 \u0111\u00f3ng vai tr\u00f2 trung gian, \u0111\u1ea3m b\u1ea3o quy\u1ec1n ri\u00eang t\u01b0 c\u1ee7a d\u1eef li\u1ec7u trong qu\u00e1 tr\u00ecnh t\u1ed1i \u01b0u h\u00f3a.<\/p>\n<\/li>\n

                  3. \n

                    Tr\u00e1nh thi\u00ean v\u1ecb<\/strong>: M\u00e1y ch\u1ee7 proxy c\u00f3 th\u1ec3 gi\u00fap \u0111\u1ea3m b\u1ea3o r\u1eb1ng c\u00e1c \u0111\u00e1nh gi\u00e1 ch\u1ee9c n\u0103ng kh\u00e1ch quan kh\u00f4ng b\u1ecb sai l\u1ec7ch d\u1ef1a tr\u00ean v\u1ecb tr\u00ed ho\u1eb7c \u0111\u1ecba ch\u1ec9 IP c\u1ee7a m\u00e1y kh\u00e1ch.<\/p>\n<\/li>\n

                  4. \n

                    C\u00e2n b\u1eb1ng t\u1ea3i<\/strong>: T\u1ed1i \u01b0u h\u00f3a Bayes c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng \u0111\u1ec3 t\u1ed1i \u01b0u h\u00f3a hi\u1ec7u su\u1ea5t v\u00e0 c\u00e2n b\u1eb1ng t\u1ea3i c\u1ee7a m\u00e1y ch\u1ee7 proxy, t\u1ed1i \u0111a h\u00f3a hi\u1ec7u qu\u1ea3 c\u1ee7a ch\u00fang trong vi\u1ec7c ph\u1ee5c v\u1ee5 c\u00e1c y\u00eau c\u1ea7u.<\/p>\n<\/li>\n<\/ol>\n

                    Li\u00ean k\u1ebft li\u00ean quan<\/h2>\n

                    \u0110\u1ec3 bi\u1ebft th\u00eam th\u00f4ng tin v\u1ec1 t\u1ed1i \u01b0u h\u00f3a Bayes, b\u1ea1n c\u00f3 th\u1ec3 kh\u00e1m ph\u00e1 c\u00e1c t\u00e0i nguy\u00ean sau:<\/p>\n

                      \n
                    1. T\u00e0i li\u1ec7u t\u1ed1i \u01b0u h\u00f3a Scikit<\/a><\/li>\n
                    2. B\u1ea1c h\u00e0: T\u1ed1i \u01b0u h\u00f3a Bayes<\/a><\/li>\n
                    3. T\u1ed1i \u01b0u h\u00f3a Bayesian th\u1ef1c t\u1ebf c\u1ee7a thu\u1eadt to\u00e1n h\u1ecdc m\u00e1y<\/a><\/li>\n<\/ol>\n

                      T\u00f3m l\u1ea1i, t\u1ed1i \u01b0u h\u00f3a Bayes l\u00e0 m\u1ed9t k\u1ef9 thu\u1eadt t\u1ed1i \u01b0u h\u00f3a m\u1ea1nh m\u1ebd v\u00e0 linh ho\u1ea1t \u0111\u00e3 t\u00ecm th\u1ea5y c\u00e1c \u1ee9ng d\u1ee5ng trong nhi\u1ec1u l\u0129nh v\u1ef1c kh\u00e1c nhau, t\u1eeb \u0111i\u1ec1u ch\u1ec9nh si\u00eau tham s\u1ed1 trong h\u1ecdc m\u00e1y \u0111\u1ebfn ch\u1ebf t\u1ea1o robot v\u00e0 kh\u00e1m ph\u00e1 thu\u1ed1c. Kh\u1ea3 n\u0103ng kh\u00e1m ph\u00e1 hi\u1ec7u qu\u1ea3 c\u00e1c kh\u00f4ng gian t\u00ecm ki\u1ebfm ph\u1ee9c t\u1ea1p v\u00e0 x\u1eed l\u00fd c\u00e1c \u0111\u00e1nh gi\u00e1 \u0111\u1eaft ti\u1ec1n khi\u1ebfn n\u00f3 tr\u1edf th\u00e0nh m\u1ed9t l\u1ef1a ch\u1ecdn h\u1ea5p d\u1eabn cho c\u00e1c nhi\u1ec7m v\u1ee5 t\u1ed1i \u01b0u h\u00f3a. Khi c\u00f4ng ngh\u1ec7 ti\u1ebfn b\u1ed9, t\u1ed1i \u01b0u h\u00f3a Bayes d\u1ef1 ki\u1ebfn s\u1ebd \u0111\u00f3ng m\u1ed9t vai tr\u00f2 ng\u00e0y c\u00e0ng quan tr\u1ecdng trong vi\u1ec7c \u0111\u1ecbnh h\u00ecnh t\u01b0\u01a1ng lai c\u1ee7a quy tr\u00ecnh t\u1ed1i \u01b0u h\u00f3a v\u00e0 h\u1ecdc m\u00e1y t\u1ef1 \u0111\u1ed9ng. Khi \u0111\u01b0\u1ee3c t\u00edch h\u1ee3p v\u1edbi m\u00e1y ch\u1ee7 proxy, t\u1ed1i \u01b0u h\u00f3a Bayesian c\u00f3 th\u1ec3 n\u00e2ng cao h\u01a1n n\u1eefa quy\u1ec1n ri\u00eang t\u01b0, b\u1ea3o m\u1eadt v\u00e0 hi\u1ec7u su\u1ea5t trong nhi\u1ec1u \u1ee9ng d\u1ee5ng.<\/p>","protected":false},"featured_media":467702,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-475994","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about Bayesian Optimization: Enhancing Efficiency and Precision<\/mark>","faq_items":[{"question":"What is Bayesian optimization?","answer":"

                      Bayesian optimization is an optimization technique used to find the best solution for complex and costly objective functions. It employs a probabilistic model, such as Gaussian Process, to approximate the objective function and iteratively selects points for evaluation to efficiently navigate the search space.<\/p>"},{"question":"How did Bayesian optimization originate?","answer":"

                      The concept of Bayesian optimization was first introduced by John Mockus in the 1970s. However, the term gained popularity in the 2000s when researchers began combining probabilistic modeling with global optimization techniques.<\/p>"},{"question":"How does Bayesian optimization work?","answer":"

                      Bayesian optimization consists of two main components: a surrogate model (often Gaussian Process) and an acquisition function. The surrogate model approximates the objective function, and the acquisition function guides the selection of the next point for evaluation based on the surrogate model's predictions and uncertainty estimates.<\/p>"},{"question":"What are the key features of Bayesian optimization?","answer":"

                      Bayesian optimization offers sample efficiency, global optimization capabilities, probabilistic representation, adaptive exploration, and the ability to handle user-defined constraints.<\/p>"},{"question":"What types of Bayesian optimization exist?","answer":"

                      There are different types of Bayesian optimization based on the surrogate model used and the optimization problem. Common types include Gaussian Process-based, Random Forest-based, and Bayesian Neural Networks-based Bayesian optimization. It can be used for both single-objective and multi-objective optimization.<\/p>"},{"question":"In what ways can Bayesian optimization be used?","answer":"

                      Bayesian optimization finds applications in hyperparameter tuning, robotics, experimental design, drug discovery, and more. It is valuable in scenarios where the objective function evaluations are expensive or time-consuming.<\/p>"},{"question":"What challenges does Bayesian optimization face?","answer":"

                      Bayesian optimization can be computationally expensive in high-dimensional spaces, and convergence to local optima may occur if the exploration-exploitation balance is not appropriately set.<\/p>"},{"question":"What technologies can enhance Bayesian optimization in the future?","answer":"

                      Future advancements in Bayesian optimization may include scalability, parallelization, transfer learning, Bayesian Neural Networks, automated machine learning, and integration with reinforcement learning algorithms.<\/p>"},{"question":"How can proxy servers be associated with Bayesian optimization?","answer":"

                      Proxy servers can be linked to Bayesian optimization by enabling distributed optimization, ensuring privacy and security during evaluations, avoiding bias, and optimizing the performance and load balancing of the proxy servers themselves.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/wiki\/475994","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/wiki\/475994\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/media\/467702"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/media?parent=475994"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}