{"id":170935,"date":"2012-04-02T08:16:07","date_gmt":"2012-04-02T08:16:07","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/project\/infer-net-fun\/"},"modified":"2026-07-01T16:09:20","modified_gmt":"2026-07-01T23:09:20","slug":"infer-net-fun","status":"publish","type":"msr-project","link":"https:\/\/www.microsoft.com\/en-us\/research\/project\/infer-net-fun\/","title":{"rendered":"Infer.NET Fun"},"content":{"rendered":"

Note<\/em><\/strong>: This research project has reached its conclusion. These pages are maintained for reference and archival purposes.<\/em><\/p>\n

“I think it’s extraordinarily important that we in computer science keep fun in computing.”<\/p>\n

Alan J. Perlis – ACM Turing Award Winner 1966.<\/p>\n

\"\"Infer.NET Fun turns the simple succinct syntax of F# into an executable modeling language for Bayesian machine learning.<\/p>\n

We propose a marriage of probabilistic functional programming with Bayesian reasoning. Infer.NET Fun turns F# into a probabilistic\u00a0modeling language \u2013 you can code up the conditional probability distributions of Bayes\u2019 rule using F# array comprehensions with constraints. Write your model in F#. Run it directly to synthesize test datasets and to debug models. Or compile it with Infer.NET for efficient statistical inference. Hence, efficient algorithms for a range of regression, classification, and specialist learning tasks derive by probabilistic functional programming.<\/p>\n

Tabular brings the power of Infer.NET Fun to spreadsheet users, via a domain-specific languages for probabilistic models designed to be authored within the spreadsheet, taking machine learning to where the data is.<\/p>\n