I will describe Infer.NET, a free software package from Microsoft that automatically applies variational Bayesian inference to a statistical model of your choosing. Unlike sampling methods, variational methods approximate the posterior distribution as a point estimate plus uncertainty, making them well suited to large-scale time-varying datasets. Infer.NET is structured as a compiler: it takes a model specification as input and produces a specialized inference program as output. This automated process makes it easy to experiment with different models and get an efficient program for each one. I will demonstrate Infer.NET with models from the psychometric literature.<\/p>\n","protected":false},"excerpt":{"rendered":"
I will describe Infer.NET, a free software package from Microsoft that automatically applies variational Bayesian inference to a statistical model of your choosing. Unlike sampling methods, variational methods approximate the posterior distribution as a point estimate plus uncertainty, making them well suited to large-scale time-varying datasets. Infer.NET is structured as a compiler: it takes a […]<\/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":[13561],"msr-publication-type":[193724],"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-352502","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-algorithms","msr-locale-en_us"],"msr_publishername":"","msr_edition":"Invited talk at the 74th Annual Meeting of the Psychometric Society (IMPS 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