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- W1034871824 abstract "An important contribution of the neural network community to the study of mixture models is the insight that the components of mixture models can be more complicated probability models than those typically used in the conventional statistics literature. This chapter reviews several members of a family of models that we refer to as “mixtures of X.” We believe that the recent development of several of these models has usefully expanded the boundaries of mixture modelling. The chapter reviews (i) mixtures of distributions from the exponential family, (ii) hidden Markov models, (iii) Mixtures of Experts, (iv) mixtures of marginal models, (v) mixtures of Cox models, (vi) mixtures of factor models, and (vii) mixtures of trees." @default.
- W1034871824 created "2016-06-24" @default.
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- W1034871824 date "1999-01-01" @default.
- W1034871824 modified "2023-09-27" @default.
- W1034871824 title "Mixtures of X" @default.
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- W1034871824 doi "https://doi.org/10.1007/978-1-4471-0793-4_11" @default.
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