{"id":164294,"date":"2013-04-01T00:00:00","date_gmt":"2013-04-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/efficiently-sampling-probabilistic-programs-via-program-analysis\/"},"modified":"2018-10-16T20:13:39","modified_gmt":"2018-10-17T03:13:39","slug":"efficiently-sampling-probabilistic-programs-via-program-analysis","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/efficiently-sampling-probabilistic-programs-via-program-analysis\/","title":{"rendered":"Efficiently Sampling Probabilistic Programs via Program Analysis"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Probabilistic programs are intuitive and succinct representations of complex probability distributions. A natural approach to performing inference over these programs is to execute them and compute statistics over the resulting samples. Indeed, this approach has been taken before in a number of probabilistic programming tools. In this paper, we address two key challenges of this paradigm: (i) ensuring samples are well distributed in the combinatorial space of the program, and (ii) efficiently generating samples with minimal rejection. We present a new sampling algorithm Qi that addresses these challenges using concepts from the fifield of program analysis. To solve the first challenge (getting diverse samples), we use a technique called symbolic execution to systematically explore all the paths in a program. In the case of programs with loops, we systematically explore all paths up to a given depth, and present theorems on error bounds on the estimates as a function of the path bounds used. To solve the second challenge (effifficient samples with minimal rejection), we propagate observations backward through the program using the notion of Dijkstra&#8217;s weakest preconditions and hoist these propagated conditions to condition elementary distributions during sampling. We present theorems explaining the mathematical properties of Qi, as well as empirical results from an implementation of the algorithm.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Probabilistic programs are intuitive and succinct representations of complex probability distributions. A natural approach to performing inference over these programs is to execute them and compute statistics over the resulting samples. Indeed, this approach has been taken before in a number of probabilistic programming tools. In this paper, we address two key challenges of this [&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":[13556,13560],"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-164294","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-research-area-programming-languages-software-engineering","msr-locale-en_us"],"msr_publishername":"","msr_edition":"Artificial Intelligence and Statistics (AISTATS)","msr_affiliation":"","msr_published_date":"2013-04-01","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","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":"205529","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","title":"105.pdf","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/105.pdf","id":205529,"label_id":0}],"msr_related_uploader":"","msr_attachments":[{"id":205529,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/105.pdf"}],"msr-author-ordering":[{"type":"text","value":"Arun T. Chaganty","user_id":0,"rest_url":false},{"type":"user_nicename","value":"adityan","user_id":30829,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=adityan"},{"type":"user_nicename","value":"sriram","user_id":33711,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=sriram"}],"msr_impact_theme":[],"msr_research_lab":[199562],"msr_event":[],"msr_group":[],"msr_project":[171174],"publication":[],"video":[],"download":[],"msr_publication_type":"inproceedings","related_content":{"projects":[{"ID":171174,"post_title":"R2: A Probabilistic Programming System","post_name":"r2-a-probabilistic-programming-system","post_type":"msr-project","post_date":"2013-07-16 23:44:21","post_modified":"2017-06-14 09:01:38","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/r2-a-probabilistic-programming-system\/","post_excerpt":"What is R2? R2 is a probabilistic programming system that uses powerful techniques from program analysis and verification for efficient Markov Chain Monte Carlo (MCMC) inference. The language that is used to describe probabilistic models in R2 is based on C#.R2 compiles the given model into executable code to generate samples from the posterior distribution. The inference algorithm currently implemented in R2 is a variation of the Metropolis-Hastings sampling algorithm. Getting R2 Click on this&hellip;","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/171174"}]}}]},"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/164294"}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-research-item"}],"version-history":[{"count":1,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/164294\/revisions"}],"predecessor-version":[{"id":524685,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/164294\/revisions\/524685"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=164294"}],"wp:term":[{"taxonomy":"msr-content-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-content-type?post=164294"},{"taxonomy":"msr-research-highlight","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-highlight?post=164294"},{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=164294"},{"taxonomy":"msr-publication-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-publication-type?post=164294"},{"taxonomy":"msr-product-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-product-type?post=164294"},{"taxonomy":"msr-focus-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-focus-area?post=164294"},{"taxonomy":"msr-platform","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-platform?post=164294"},{"taxonomy":"msr-download-source","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-download-source?post=164294"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=164294"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=164294"},{"taxonomy":"msr-field-of-study","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-field-of-study?post=164294"},{"taxonomy":"msr-conference","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-conference?post=164294"},{"taxonomy":"msr-journal","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-journal?post=164294"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=164294"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=164294"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}