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{ "category": "SciPy 2012", "language": "English", "slug": "sympy-stats-uncertainty-modeling", "speakers": [ "Matthew Rocklin" ], "tags": [ "General" ], "id": 1208, "state": 1, "title": "SymPy Stats - Uncertainty Modeling", "summary": "", "description": "SymPy is a symbolic algebra package for Python. In SymPy.Stats we add a\nstochastic variable type to this package to form a language for uncertainty\nmodeling. This allows engineers and scientists to symbolically declare the\nuncertainty in their mathematical models and to make probabilistic queries. We\nprovide transformations from probabilistic statements like $P(X*Y > 3)$ or\n$E(X**2)$ into deterministic integrals. These integrals are then solved using\nSymPy's integration routines or through numeric sampling.\n\nThis talk touches on a few rising themes:\n\n \n\n * The rise in interest in uncertainty quantification and\n * The use of symbolics in scientific computing\n * Intermediate representation layers and multi-stage compilation\n\nHistorically solutions to uncertainty quantification problems have been\nexpressed by writing Monte Carlo codes around individual problems. By creating\na symbolic uncertainty language we allow the expression of the problem-to-be-\nsolved to be written separately from the numerical technique. SymPy.stats\nserves as an interface layer. The statistical programmer doesn't need to think\nabout the details of numerical techniques and the computational methods\nprogrammer doesn't need to think about the particular domain-specific\nquestions to be solved.\n\nWe have implemented multiple comptuational backends including purely symbolic\n(using SymPy's integration engine), sampling, and code generation.\n\nIn the talk we discuss these ideas with a few illustrative examples taken from\nbasic probability and engineering. The following is one such example\n\n[http://sympystats.wordpress.com/2011/07/02/a-lesson-in-data-assimilation-\nusing-sympy/](http://sympystats.wordpress.com/2011/07/02/a-lesson-in-data-\nassimilation-using-sympy/)\n\n", "quality_notes": "", "copyright_text": "CC BY-SA", "embed": "<object width=\"640\" height=\"390\"><param name=\"movie\" value=\"http://youtube.com/v/27su3TQ3BvQ?version=3&amp;hl=en_US\"></param><param name=\"allowFullScreen\" value=\"true\"></param><param name=\"allowscriptaccess\" value=\"always\"></param><embed src=\"http://youtube.com/v/27su3TQ3BvQ?version=3&amp;hl=en_US\" type=\"application/x-shockwave-flash\" width=\"640\" height=\"390\" allowscriptaccess=\"always\" allowfullscreen=\"true\"></embed></object>", "thumbnail_url": "http://i3.ytimg.com/vi/27su3TQ3BvQ/hqdefault.jpg", "duration": null, "video_ogv_length": null, "video_ogv_url": null, "video_ogv_download_only": false, "video_mp4_length": null, "video_mp4_url": "http://s3.us.archive.org/nextdayvideo/enthought/scipy_2012/SymPy_Stats_Uncertainty_Modeling.mp4?Signature=rl3GFBy%2FFvQFU4vEkWldM59hlQA%3D&Expires=1346381534&AWSAccessKeyId=FEWGReWX3QbNk0h3", "video_mp4_download_only": false, "video_webm_length": null, "video_webm_url": "", "video_webm_download_only": false, "video_flv_length": null, "video_flv_url": "", "video_flv_download_only": false, "source_url": "http://youtube.com/watch?v=27su3TQ3BvQ", "whiteboard": "", "recorded": "2012-07-18", "added": "2012-08-31T16:34:59", "updated": "2014-04-08T20:28:27.163" }