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The toolbox provides functions for the following problems: | The toolbox provides functions for the following problems: | ||
- | - normal mean and variance estimation from trapezoidal fuzzy data; | ||
- | - multiple linear regression with crisp inputs and trapezoidal fuzzy outputs; | ||
- | - univariate finite normal mixture estimation from trapezoidal fuzzy data. | ||
- | References: | + | * normal mean and variance estimation from trapezoidal fuzzy data; |
+ | * multiple linear regression with crisp inputs and trapezoidal fuzzy outputs; | ||
+ | * univariate finite normal mixture estimation from trapezoidal fuzzy data. | ||
- | - T. Denoeux. Maximum likelihood from evidential data: an extension of the EM algorithm. In C. Borgelt et al. (Eds), Combining soft computing and statistical methods in data analysis (Proceedings of SMPS 2010, Oviedo, Spain, September 28 - October 1, 2010), Advances in Intelligent and Soft Computing, pages 181-188, Springer, 2010. {{en: | + | Reference: |
- | - T. Denoeux, Maximum likelihood estimation from Uncertain Data in the Belief Function Framework, IEEE Transactions on Knowledge and Data Engineering (to appear), 2011. | + | |
- | {{en: | + | T. Denoeux. Maximum likelihood estimation from fuzzy data using the EM algorithm. Fuzzy Sets and Systems, accepted for publication, |
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