{"id":352637,"date":"2017-01-13T17:02:07","date_gmt":"2017-01-14T01:02:07","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=352637"},"modified":"2018-10-16T20:18:57","modified_gmt":"2018-10-17T03:18:57","slug":"estimating-dirichlet-distribution","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/estimating-dirichlet-distribution\/","title":{"rendered":"Estimating a Dirichlet Distribution"},"content":{"rendered":"

The Dirichlet distribution and its compound variant, the Dirichlet-multinomial, are two of the most basic models for proportional data, such as the mix of vocabulary words in a text document. Yet the maximum-likelihood estimate of these distributions is not available in closed-form. This paper describes simple and efficient iterative schemes for obtaining parameter estimates in these models. In each case, a fixed-point iteration and a Newton-Raphson (or generalized Newton-Raphson) iteration is provided.<\/p>\n","protected":false},"excerpt":{"rendered":"

The Dirichlet distribution and its compound variant, the Dirichlet-multinomial, are two of the most basic models for proportional data, such as the mix of vocabulary words in a text document. Yet the maximum-likelihood estimate of these distributions is not available in closed-form. This paper describes simple and efficient iterative schemes for obtaining parameter estimates in 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