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ACM Program. Lang."],"published-print":{"date-parts":[[2017,8,29]]},"abstract":"<jats:p>Probabilistic programming systems make machine learning more modular by automating<jats:italic>inference<\/jats:italic>. Recent work by Shan and Ramsey makes inference more modular by automating<jats:italic>conditioning<\/jats:italic>. Their technique uses a symbolic program transformation that treats conditioning generally via the measure-theoretic notion of<jats:italic>disintegration<\/jats:italic>. This technique, however, is limited to conditioning a single scalar variable. As a step towards modular inference for realistic machine learning applications, we have extended the disintegration algorithm to symbolically condition arrays in probabilistic programs. The extended algorithm implements<jats:italic>lifted disintegration<\/jats:italic>, where repetition is treated symbolically and without unrolling loops. The technique uses a language of<jats:italic>index variables<\/jats:italic>for tracking expressions at various array levels. We find that the method works well for arbitrarily-sized arrays of independent random choices, with the conditioning step taking time linear in the number of indices needed to select an element.<\/jats:p>","DOI":"10.1145\/3110255","type":"journal-article","created":{"date-parts":[[2017,8,29]],"date-time":"2017-08-29T18:19:41Z","timestamp":1504030781000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Symbolic conditioning of arrays in probabilistic programs"],"prefix":"10.1145","volume":"1","author":[{"given":"Praveen","family":"Narayanan","sequence":"first","affiliation":[{"name":"Indiana University, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chung-chieh","family":"Shan","sequence":"additional","affiliation":[{"name":"Indiana University, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2017,8,29]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/LICS.2011.49"},{"key":"e_1_2_2_2_1","unstructured":"Arthur Asuncion Max Welling Padhraic Smyth and Yee-Whye Teh. 2009. 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