{"id":49,"date":"2019-06-30T22:17:58","date_gmt":"2019-06-30T22:17:58","guid":{"rendered":"http:\/\/randomfields.org\/?page_id=49"},"modified":"2019-07-02T22:35:15","modified_gmt":"2019-07-02T22:35:15","slug":"probabilistic-machine-learning","status":"publish","type":"page","link":"https:\/\/randomfields.org\/","title":{"rendered":"Probabilistic Machine Learning"},"content":{"rendered":"<div id=\"themify_builder_content-49\" data-postid=\"49\" class=\"themify_builder_content themify_builder_content-49 themify_builder\">\n    \t<!-- module_row -->\n\t<div  class=\"themify_builder_row module_row clearfix module_row_0 themify_builder_49_row module_row_49-0 tb_f1gv646\">\n\t    \t    <div class=\"row_inner col_align_top\" >\n\t\t\t<div  class=\"module_column tb-column col-full first tb_49_column module_column_0 module_column_49-0-0 tb_dnsu647 repeat\" >\n\t    \t    \t        <div class=\"tb-column-inner\">\n\t\t    <!-- module text -->\n<div  class=\"module module-text tb_xaz19    \">\n            <div  class=\"tb_text_wrap\">\n    <p style=\"text-align: left;\"><strong>Why probabilistic ML?<\/strong><\/p>\n<ul>\n<li>Consistent estimation: guaranteed to benefit from large data sets<\/li>\n<li>Interpretable model weights, interpretable latent space<\/li>\n<li>Sufficient statistics: fast learning from arbitrary large data sets<\/li>\n<li>Generative modelling: ready to sample new data, detect novelties, replace missing values<\/li>\n<li>Probabilistic inference: report marginal probabilities for interesting sub-sets of variables, perform maximum a posteriori prediction<\/li>\n<li>Expectation maximization: learn from missing data<\/li>\n<\/ul>    <\/div>\n<\/div>\n<!-- \/module text -->\n\t        <\/div>\n\t    \t<\/div>\n\t\t    <\/div>\n\t    <!-- \/row_inner -->\n\t<\/div>\n\t<!-- \/module_row -->\n\t<\/div>\n\n","protected":false},"excerpt":{"rendered":"<p>Why probabilistic ML? Consistent estimation: guaranteed to benefit from large data sets Interpretable model weights, interpretable latent space Sufficient statistics: fast learning from arbitrary large data sets Generative modelling: ready to sample new data, detect novelties, replace missing values Probabilistic inference: report marginal probabilities for interesting sub-sets of variables, perform maximum a posteriori prediction Expectation [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":1,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"builder_content":"<p style=\"text-align: left;\"><strong>Why probabilistic ML?<\/strong><\/p> <ul> <li>Consistent estimation: guaranteed to benefit from large data sets<\/li> <li>Interpretable model weights, interpretable latent space<\/li> <li>Sufficient statistics: fast learning from arbitrary large data sets<\/li> <li>Generative modelling: ready to sample new data, detect novelties, replace missing values<\/li> <li>Probabilistic inference: report marginal probabilities for interesting sub-sets of variables, perform maximum a posteriori prediction<\/li> <li>Expectation maximization: learn from missing data<\/li> <\/ul>","_links":{"self":[{"href":"https:\/\/randomfields.org\/index.php?rest_route=\/wp\/v2\/pages\/49"}],"collection":[{"href":"https:\/\/randomfields.org\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/randomfields.org\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/randomfields.org\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/randomfields.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=49"}],"version-history":[{"count":23,"href":"https:\/\/randomfields.org\/index.php?rest_route=\/wp\/v2\/pages\/49\/revisions"}],"predecessor-version":[{"id":116,"href":"https:\/\/randomfields.org\/index.php?rest_route=\/wp\/v2\/pages\/49\/revisions\/116"}],"wp:attachment":[{"href":"https:\/\/randomfields.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=49"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}