{"id":149950,"date":"1995-01-01T00:00:00","date_gmt":"1995-01-01T08:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/estimating-conditional-probability-densities-for-periodic-variables\/"},"modified":"2018-10-16T21:49:34","modified_gmt":"2018-10-17T04:49:34","slug":"estimating-conditional-probability-densities-for-periodic-variables","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/estimating-conditional-probability-densities-for-periodic-variables\/","title":{"rendered":"Estimating conditional probability densities for periodic variables"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Most of the common techniques for estimating conditional probability densities are inappropriate for applications involving periodic variables. In this paper we introduce three novel techniques for tackling such problems, and investigate their performance using synthetic data. We then apply these techniques to the problem of extracting the distribution of wind vector directions from radar scatterometer data gathered by a remote-sensing satellite.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Most of the common techniques for estimating conditional probability densities are inappropriate for applications involving periodic variables. In this paper we introduce three novel techniques for tackling such problems, and investigate their performance using synthetic data. We then apply these techniques to the problem of extracting the distribution of wind vector directions from radar scatterometer [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13556],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-149950","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-locale-en_us"],"msr_publishername":"MIT Press","msr_edition":"Advances in Neural Information Processing Systems","msr_affiliation":"","msr_published_date":"1995-01-01","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"Advances in Neural Information Processing Systems","msr_pages_string":"641\u2013648","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"7","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"211751","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","title":"bishop-periodic-nips-95.pdf","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/bishop-periodic-nips-95.pdf","id":211751,"label_id":0}],"msr_related_uploader":"","msr_attachments":[{"id":211751,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/bishop-periodic-nips-95.pdf"}],"msr-author-ordering":[{"type":"user_nicename","value":"cmbishop","user_id":31452,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=cmbishop"},{"type":"text","value":"C. 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