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{ "category": "SciPy 2013", "language": "English", "slug": "inferpy-probabilistic-programming-and-bayesian-1", "speakers": [], "tags": [ "Tech" ], "id": 2105, "state": 1, "title": " Probabilistic Programming and Bayesian Inference from Python; SciPy 2013 Presentation", "summary": "Authors: Zinkov, Rob\n\nTrack: Machine Learning\n\ is a wrapper around Microsoft Research's Infer.NET inference engine. allows you to represent complex graphical models in terms of short pieces of code. In this talk, I will show how many popular machine learning algorithms can be modeled as short probabilistic programs and then simply trained. I will then show how to introspect on the models which were learned and debug these programs when they don't produce desired results.", "description": "", "quality_notes": "", "copyright_text": "", "embed": "<object width=\"640\" height=\"390\"><param name=\"movie\" value=\";hl=en_US\"></param><param name=\"allowFullScreen\" value=\"true\"></param><param name=\"allowscriptaccess\" value=\"always\"></param><embed src=\";hl=en_US\" type=\"application/x-shockwave-flash\" width=\"640\" height=\"390\" allowscriptaccess=\"always\" allowfullscreen=\"true\"></embed></object>", "thumbnail_url": "", "duration": null, "video_ogv_length": null, "video_ogv_url": null, "video_ogv_download_only": false, "video_mp4_length": null, "video_mp4_url": null, "video_mp4_download_only": false, "video_webm_length": null, "video_webm_url": null, "video_webm_download_only": false, "video_flv_length": null, "video_flv_url": null, "video_flv_download_only": false, "source_url": "", "whiteboard": "needs editing", "recorded": "2013-07-01", "added": "2013-07-04T10:08:54", "updated": "2014-04-08T20:28:26.406" }