{"id":477450,"date":"2023-08-09T09:15:09","date_gmt":"2023-08-09T09:15:09","guid":{"rendered":""},"modified":"2023-09-05T11:14:43","modified_gmt":"2023-09-05T11:14:43","slug":"hidden-markov-models","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/kr\/wiki\/hidden-markov-models\/","title":{"rendered":"\uc228\uaca8\uc9c4 \ub9c8\ub974\ucf54\ud504 \ubaa8\ub378"},"content":{"rendered":"

HMM(Hidden Markov Model)\uc740 \uc2dc\uac04\uc774 \uc9c0\ub0a8\uc5d0 \ub530\ub77c \uc9c4\ud654\ud558\ub294 \uc2dc\uc2a4\ud15c\uc744 \ub098\ud0c0\ub0b4\ub294 \ub370 \uc0ac\uc6a9\ub418\ub294 \ud1b5\uacc4 \ubaa8\ub378\uc785\ub2c8\ub2e4. \ubcf5\uc7a1\ud558\uace0 \uc2dc\uac04\uc5d0 \ub530\ub978 \ud655\ub960\ub860\uc801 \uacfc\uc815\uc744 \ubaa8\ub378\ub9c1\ud558\ub294 \ub2a5\ub825 \ub355\ubd84\uc5d0 \uae30\uacc4 \ud559\uc2b5, \ud328\ud134 \uc778\uc2dd, \uc804\uc0b0 \uc0dd\ubb3c\ud559\uacfc \uac19\uc740 \ubd84\uc57c\uc5d0\uc11c \uc790\uc8fc \uc0ac\uc6a9\ub429\ub2c8\ub2e4.<\/p>\n

\uc2dc\uc791 \ucd94\uc801: \uc740\ub2c9 \ub9c8\ub974\ucf54\ud504 \ubaa8\ub378\uc758 \uae30\uc6d0\uacfc \uc9c4\ud654<\/h2>\n

\uc740\ub2c9 \ub9c8\ub974\ucf54\ud504 \ubaa8\ub378\uc758 \uc774\ub860\uc801 \ud2c0\uc740 1960\ub144\ub300 \ud6c4\ubc18 Leonard E. Baum\uacfc \uadf8\uc758 \ub3d9\ub8cc\ub4e4\uc5d0 \uc758\ud574 \ucc98\uc74c \uc81c\uc548\ub418\uc5c8\uc2b5\ub2c8\ub2e4. \ucc98\uc74c\uc5d0\ub294 \uc74c\uc131 \uc778\uc2dd \uae30\uc220\uc5d0 \uc0ac\uc6a9\ub418\uc5c8\uc73c\uba70 1970\ub144\ub300 IBM\uc774 \ucd5c\ucd08\uc758 \uc74c\uc131 \uc778\uc2dd \uc2dc\uc2a4\ud15c\uc5d0 \uc0ac\uc6a9\ud558\uba74\uc11c \uc778\uae30\ub97c \uc5bb\uc5c8\uc2b5\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ubaa8\ub378\uc740 \uadf8 \uc774\ud6c4\ub85c \uacc4\uc18d \uc870\uc815\ub418\uace0 \ud5a5\uc0c1\ub418\uc5b4 \uc778\uacf5 \uc9c0\ub2a5 \ubc0f \uae30\uacc4 \ud559\uc2b5 \uac1c\ubc1c\uc5d0 \ud06c\uac8c \uae30\uc5ec\ud588\uc2b5\ub2c8\ub2e4.<\/p>\n

\uc740\ub2c9 \ub9c8\ub974\ucf54\ud504 \ubaa8\ub378: \uc228\uaca8\uc9c4 \uae4a\uc774 \uacf5\uac1c<\/h2>\n

HMM\uc740 \uad00\ucc30\ub418\uc9c0 \uc54a\uac70\ub098 "\uc228\uaca8\uc9c4" \ubcc0\uc218 \uc9d1\ud569\uc758 \uc5ed\ud559\uc744 \uae30\ubc18\uc73c\ub85c \uad00\ucc30\ub41c \ubcc0\uc218 \uc9d1\ud569\uc5d0 \ub300\ud55c \uc608\uce21, \ud544\ud130\ub9c1, \ud3c9\ud65c\ud654 \ubc0f \uc124\uba85 \ucc3e\uae30\uc640 \uad00\ub828\ub41c \ubb38\uc81c\uc5d0 \ud2b9\ud788 \uc801\ud569\ud569\ub2c8\ub2e4. \uc774\ub294 \ubaa8\ub378\ub9c1\ub418\ub294 \uc2dc\uc2a4\ud15c\uc774 \uad00\ucc30\ud560 \uc218 \uc5c6\ub294("\uc228\uaca8\uc9c4") \uc0c1\ud0dc\ub97c \uac16\ub294 Markov \ud504\ub85c\uc138\uc2a4, \uc989 \uba54\ubaa8\ub9ac \uc5c6\ub294 \ubb34\uc791\uc704 \ud504\ub85c\uc138\uc2a4\ub85c \uac00\uc815\ub418\ub294 Markov \ubaa8\ub378\uc758 \ud2b9\ubcc4\ud55c \uacbd\uc6b0\uc785\ub2c8\ub2e4.<\/p>\n

\ubcf8\uc9c8\uc801\uc73c\ub85c HMM\uc744 \uc0ac\uc6a9\ud558\uba74 \uad00\ucc30\ub41c \uc774\ubca4\ud2b8(\uc785\ub825\uc5d0\uc11c \ubcf4\ub294 \ub2e8\uc5b4 \ub4f1)\uc640 \uad00\ucc30\ub41c \uc774\ubca4\ud2b8\uc758 \uc6d0\uc778 \uc694\uc18c\ub85c \uc0dd\uac01\ub418\ub294 \uc228\uaca8\uc9c4 \uc774\ubca4\ud2b8(\ubb38\ubc95 \uad6c\uc870 \ub4f1)\uc5d0 \ub300\ud574 \ubaa8\ub450 \uc774\uc57c\uae30\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

\ub0b4\ubd80 \uc791\ub3d9 \ubc29\uc2dd: \uc740\ub2c9 \ub9c8\ub974\ucf54\ud504 \ubaa8\ub378\uc758 \uc791\ub3d9 \ubc29\uc2dd<\/h2>\n

HMM\uc758 \ub0b4\ubd80 \uad6c\uc870\ub294 \ub450 \uac00\uc9c0 \uae30\ubcf8 \ubd80\ubd84\uc73c\ub85c \uad6c\uc131\ub429\ub2c8\ub2e4.<\/p>\n

    \n
  1. \uad00\ucc30 \uac00\ub2a5\ud55c \ubcc0\uc218\uc758 \uc2dc\ud000\uc2a4<\/li>\n
  2. \uc228\uaca8\uc9c4 \ubcc0\uc218\uc758 \uc2dc\ud000\uc2a4<\/li>\n<\/ol>\n

    \uc740\ub2c9 \ub9c8\ub974\ucf54\ud504 \ubaa8\ub378\uc5d0\ub294 \uc0c1\ud0dc\uac00 \uc9c1\uc811 \ud45c\uc2dc\ub418\uc9c0 \uc54a\uc9c0\ub9cc \uc0c1\ud0dc\uc5d0 \ub530\ub978 \ucd9c\ub825\uc774 \ud45c\uc2dc\ub418\ub294 \ub9c8\ub974\ucf54\ud504 \ud504\ub85c\uc138\uc2a4\uac00 \ud3ec\ud568\ub418\uc5b4 \uc788\uc2b5\ub2c8\ub2e4. \uac01 \uc0c1\ud0dc\uc5d0\ub294 \uac00\ub2a5\ud55c \ucd9c\ub825 \ud1a0\ud070\uc5d0 \ub300\ud55c \ud655\ub960 \ubd84\ud3ec\uac00 \uc788\uc2b5\ub2c8\ub2e4. \ub530\ub77c\uc11c HMM\uc5d0\uc11c \uc0dd\uc131\ub41c \ud1a0\ud070 \uc2dc\ud000\uc2a4\ub294 \uc0c1\ud0dc \uc2dc\ud000\uc2a4\uc5d0 \ub300\ud55c \uc77c\ubd80 \uc815\ubcf4\ub97c \uc81c\uacf5\ud558\uc5ec \uc774\uc911\uc73c\ub85c \ud3ec\ud568\ub41c \ud655\ub960\ub860\uc801 \ud504\ub85c\uc138\uc2a4\ub97c \ub9cc\ub4ed\ub2c8\ub2e4.<\/p>\n

    \uc740\ub2c9 \ub9c8\ub974\ucf54\ud504 \ubaa8\ub378\uc758 \uc8fc\uc694 \ud2b9\uc9d5<\/h2>\n

    \uc740\ub2c9 \ub9c8\ub974\ucf54\ud504 \ubaa8\ub378\uc758 \ud544\uc218 \ud2b9\uc131\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.<\/p>\n

      \n
    1. \uad00\ucc30 \uac00\ub2a5\uc131: \uc2dc\uc2a4\ud15c \uc0c1\ud0dc\ub97c \uc9c1\uc811 \uad00\ucc30\ud560 \uc218 \uc5c6\uc2b5\ub2c8\ub2e4.<\/li>\n
    2. \ub9c8\ub974\ucf54\ud504 \uc18d\uc131: \uac01 \uc0c1\ud0dc\ub294 \uc774\uc804 \uc0c1\ud0dc\uc758 \uc720\ud55c\ud55c \uc5ed\uc0ac\uc5d0\ub9cc \uc758\uc874\ud569\ub2c8\ub2e4.<\/li>\n
    3. \uc2dc\uac04 \uc758\uc874\uc131: \ud655\ub960\uc740 \uc2dc\uac04\uc774 \uc9c0\ub0a8\uc5d0 \ub530\ub77c \ubcc0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n
    4. \uc0dd\uc131\uc131: HMM\uc740 \uc0c8\ub85c\uc6b4 \uc2dc\ud000\uc2a4\ub97c \uc0dd\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n<\/ol>\n

      \uc740\ub2c9 \ub9c8\ub974\ucf54\ud504 \ubaa8\ub378 \ubd84\ub958: \ud45c \ud615\uc2dd \uac1c\uc694<\/h2>\n

      \uc740\ub2c9 \ub9c8\ub974\ucf54\ud504 \ubaa8\ub378\uc5d0\ub294 \uc138 \uac00\uc9c0 \uae30\ubcf8 \uc720\ud615\uc774 \uc788\uc73c\uba70, \ud65c\uc6a9\ud558\ub294 \uc0c1\ud0dc \uc804\uc774 \ud655\ub960 \ubd84\ud3ec \uc720\ud615\uc5d0 \ub530\ub77c \uad6c\ubcc4\ub429\ub2c8\ub2e4.<\/p>\n\n\n\n\n\n\n\n
      \uc720\ud615<\/th>\n\uc124\uba85<\/th>\n<\/tr>\n<\/thead>\n
      \uc5d0\ub974\uace0\ub515<\/td>\n\ubaa8\ub4e0 \uc0c1\ud0dc\ub294 \ubaa8\ub4e0 \uc0c1\ud0dc\uc5d0\uc11c \uc811\uadfc \uac00\ub2a5\ud569\ub2c8\ub2e4.<\/td>\n<\/tr>\n
      \uc67c\ucabd \uc624\ub978\ucabd<\/td>\n\uc77c\ubc18\uc801\uc73c\ub85c \uc815\ubc29\ud5a5\uc73c\ub85c \ud2b9\uc815 \uc804\ud658\uc774 \ud5c8\uc6a9\ub429\ub2c8\ub2e4.<\/td>\n<\/tr>\n
      \uc644\uc804\ud788 \uc5f0\uacb0\ub428<\/td>\n\ubaa8\ub4e0 \uc0c1\ud0dc\ub294 \ud55c \ubc88\uc758 \ub2e8\uacc4\ub85c \ub2e4\ub978 \uc0c1\ud0dc\uc5d0\uc11c \ub3c4\ub2ec\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

      \uc740\ub2c9 \ub9c8\ub974\ucf54\ud504 \ubaa8\ub378\uacfc \uad00\ub828\ub41c \ud65c\uc6a9, \uacfc\uc81c \ubc0f \uc194\ub8e8\uc158<\/h2>\n

      \uc740\ub2c9 \ub9c8\ub974\ucf54\ud504 \ubaa8\ub378\uc740 \uc74c\uc131 \uc778\uc2dd, \uc0dd\ubb3c\uc815\ubcf4\ud559, \ub0a0\uc528 \uc608\uce21 \ub4f1 \ub2e4\uc591\ud55c \uc751\uc6a9 \ubd84\uc57c\uc5d0 \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \uadf8\ub7ec\ub098 \ub192\uc740 \uacc4\uc0b0 \ube44\uc6a9, \uc228\uaca8\uc9c4 \uc0c1\ud0dc \ud574\uc11d\uc758 \uc5b4\ub824\uc6c0, \ubaa8\ub378 \uc120\ud0dd \ubb38\uc81c\uc640 \uac19\uc740 \uacfc\uc81c\ub3c4 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

      \uc774\ub7ec\ud55c \ubb38\uc81c\ub97c \uc644\ud654\ud558\uae30 \uc704\ud574 \uc5ec\ub7ec \uac00\uc9c0 \uc194\ub8e8\uc158\uc774 \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4 Baum-Welch \uc54c\uace0\ub9ac\uc998\uacfc Viterbi \uc54c\uace0\ub9ac\uc998\uc740 HMM\uc5d0\uc11c \ud559\uc2b5 \ubc0f \ucd94\ub860 \ubb38\uc81c\ub97c \ud6a8\uc728\uc801\uc73c\ub85c \ud574\uacb0\ud558\ub294 \ub370 \ub3c4\uc6c0\uc774 \ub429\ub2c8\ub2e4.<\/p>\n

      \ube44\uad50 \ubc0f \ud2b9\uc9d5: HMM \ubc0f \uc720\uc0ac \ubaa8\ub378<\/h2>\n

      DBN(Dynamic Bayesian Networks) \ubc0f RNN(Recurrent Neural Networks)\uacfc \uac19\uc740 \uc720\uc0ac\ud55c \ubaa8\ub378\uacfc \ube44\uad50\ud560 \ub54c HMM\uc740 \ud2b9\uc815\ud55c \uc7a5\uc810\uacfc \ud55c\uacc4\ub97c \uac00\uc9c0\uace0 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n\n\n\n\n\n\n\n
      \ubaa8\ub378<\/th>\n\uc7a5\uc810<\/th>\n\uc81c\ud55c\uc0ac\ud56d<\/th>\n<\/tr>\n<\/thead>\n
      \uc228\uaca8\uc9c4 \ub9c8\ub974\ucf54\ud504 \ubaa8\ub378<\/td>\n\uc2dc\uacc4\uc5f4 \ub370\uc774\ud130 \ubaa8\ub378\ub9c1\uc5d0 \ub2a5\uc219\ud558\uba70 \uc774\ud574 \ubc0f \uad6c\ud604\uc774 \uac04\ub2e8\ud569\ub2c8\ub2e4.<\/td>\n\uc77c\ubd80 \uc751\uc6a9 \ud504\ub85c\uadf8\ub7a8\uc5d0\uc11c\ub294 Markov \uc18d\uc131\uc758 \uac00\uc815\uc774 \ub108\ubb34 \uc81c\ud55c\uc801\uc77c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/td>\n<\/tr>\n
      \ub3d9\uc801 \ubca0\uc774\uc9c0\uc548 \ub124\ud2b8\uc6cc\ud06c<\/td>\nHMM\ubcf4\ub2e4 \uc720\uc5f0\ud558\uba70 \ubcf5\uc7a1\ud55c \uc2dc\uac04\uc801 \uc885\uc18d\uc131\uc744 \ubaa8\ub378\ub9c1\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/td>\n\ubc30\uc6b0\uace0 \uad6c\ud604\ud558\uae30\uac00 \ub354 \uc5b4\ub835\uc2b5\ub2c8\ub2e4.<\/td>\n<\/tr>\n
      \uc21c\ud658 \uc2e0\uacbd\ub9dd<\/td>\n\uae34 \uc2dc\ud000\uc2a4 \ucc98\ub9ac \uac00\ub2a5, \ubcf5\uc7a1\ud55c \uae30\ub2a5 \ubaa8\ub378\ub9c1 \uac00\ub2a5<\/td>\n\ub9ce\uc740 \uc591\uc758 \ub370\uc774\ud130\uac00 \ud544\uc694\ud558\uba70 \ud6c8\ub828\uc774 \uc5b4\ub824\uc6b8 \uc218 \uc788\uc74c<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

      \ubbf8\ub798\uc758 \uc9c0\ud3c9: \uc228\uaca8\uc9c4 \ub9c8\ub974\ucf54\ud504 \ubaa8\ub378 \ubc0f \uc2e0\ud765 \uae30\uc220<\/h2>\n

      Hidden Markov \ubaa8\ub378\uc758 \ud5a5\ud6c4 \ubc1c\uc804\uc5d0\ub294 \uc228\uaca8\uc9c4 \uc0c1\ud0dc\ub97c \ub354 \uc798 \ud574\uc11d\ud558\ub294 \ubc29\ubc95, \uacc4\uc0b0 \ud6a8\uc728\uc131 \uac1c\uc120, \uc591\uc790 \ucef4\ud4e8\ud305 \ubc0f \uace0\uae09 AI \uc54c\uace0\ub9ac\uc998\uacfc \uac19\uc740 \uc0c8\ub85c\uc6b4 \uc751\uc6a9 \ubd84\uc57c\ub85c\uc758 \ud655\uc7a5\uc774 \ud3ec\ud568\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

      \ud504\ub85d\uc2dc \uc11c\ubc84\uc640 \uc228\uaca8\uc9c4 \ub9c8\ub974\ucf54\ud504 \ubaa8\ub378: \uc0c9\ub2e4\ub978 \ub3d9\ub9f9<\/h2>\n

      \uc228\uaca8\uc9c4 \ub9c8\ub974\ucf54\ud504 \ubaa8\ub378\uc740 \ud504\ub85d\uc2dc \uc11c\ubc84\uc758 \uadc0\uc911\ud55c \uae30\ub2a5\uc778 \ub124\ud2b8\uc6cc\ud06c \ud2b8\ub798\ud53d \ud328\ud134\uc744 \ubd84\uc11d\ud558\uace0 \uc608\uce21\ud558\ub294 \ub370 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ud504\ub85d\uc2dc \uc11c\ubc84\ub294 HMM\uc744 \ud65c\uc6a9\ud558\uc5ec \ud2b8\ub798\ud53d\uc744 \ubd84\ub958\ud558\uace0 \uc774\uc0c1 \uc9d5\ud6c4\ub97c \uac10\uc9c0\ud558\uc5ec \ubcf4\uc548\uacfc \ud6a8\uc728\uc131\uc744 \ud5a5\uc0c1\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

      \uad00\ub828\ub41c \ub9c1\ud06c\ub4e4<\/h2>\n

      Hidden Markov \ubaa8\ub378\uc5d0 \ub300\ud55c \uc790\uc138\ud55c \ub0b4\uc6a9\uc744 \ubcf4\ub824\uba74 \ub2e4\uc74c \ub9ac\uc18c\uc2a4\ub97c \ubc29\ubb38\ud558\ub294 \uac83\uc774 \uc88b\uc2b5\ub2c8\ub2e4.<\/p>\n

        \n
      1. \uc740\ub2c9 \ub9c8\ub974\ucf54\ud504 \ubaa8\ub378(\uc2a4\ud0e0\ud3ec\ub4dc \ub300\ud559)<\/a><\/li>\n
      2. Hidden Markov \ubaa8\ub378\uc5d0 \ub300\ud55c \ud29c\ud1a0\ub9ac\uc5bc(University of Leeds)<\/a><\/li>\n
      3. \uc740\ub2c9 \ub9c8\ub974\ucf54\ud504 \ubaa8\ub378(MIT) \uc18c\uac1c<\/a><\/li>\n
      4. \uc740\ub2c9 \ub9c8\ub974\ucf54\ud504 \ubaa8\ub378 \ud559\uc2b5(\uc790\uc5f0)<\/a><\/li>\n<\/ol>","protected":false},"featured_media":468545,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-477450","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about Hidden Markov Models: Unraveling the Invisible Patterns<\/mark>","faq_items":[{"question":"What is a Hidden Markov Model?","answer":"

        A Hidden Markov Model is a statistical model that is used to represent systems that evolve over time. They are well-suited to problems involving prediction, filtering, smoothing, and finding explanations for a set of observed variables based on the dynamics of an unobserved or \"hidden\" set of variables.<\/p>"},{"question":"Who first proposed the concept of Hidden Markov Models?","answer":"

        The theoretical framework of Hidden Markov Models was first proposed in the late 1960s by Leonard E. Baum and his colleagues.<\/p>"},{"question":"What are the key features of Hidden Markov Models?","answer":"

        The essential features of Hidden Markov Models include observability, the Markov property, time dependence, and generativity. The system's states are not directly observable, each state depends only on a finite history of previous states, the probabilities can change over time, and HMMs can generate new sequences.<\/p>"},{"question":"What are the types of Hidden Markov Models?","answer":"

        There are three primary types of Hidden Markov Models: Ergodic, in which all states are reachable from any state; Left-right, where specific transitions are allowed, typically in a forward direction; and Fully connected, where any state can be reached from any other state in one time step.<\/p>"},{"question":"What are the common applications of Hidden Markov Models?","answer":"

        Hidden Markov Models are used in a variety of applications, including speech recognition, bioinformatics, and weather prediction.<\/p>"},{"question":"What challenges are associated with the use of Hidden Markov Models?","answer":"

        Challenges associated with Hidden Markov Models include high computational cost, difficulty in interpreting hidden states, and issues with model selection.<\/p>"},{"question":"How are Hidden Markov Models related to proxy servers?","answer":"

        Hidden Markov Models can be used to analyze and predict network traffic patterns, which is valuable for proxy servers. Proxy servers can utilize HMMs to classify traffic and detect anomalies, thus improving security and efficiency.<\/p>"},{"question":"What is the future perspective of Hidden Markov Models?","answer":"

        Future advancements in Hidden Markov Models may include methods to better interpret hidden states, improvements in computation efficiency, and expansion into new areas of application like quantum computing and advanced AI algorithms.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/kr\/wp-json\/wp\/v2\/wiki\/477450","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/kr\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/kr\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/kr\/wp-json\/wp\/v2\/wiki\/477450\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/kr\/wp-json\/wp\/v2\/media\/468545"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/kr\/wp-json\/wp\/v2\/media?parent=477450"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}