About: Dependent Dirichlet process

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In the mathematical theory of probability, the dependent Dirichlet process (DDP) provides a non-parametric prior over evolving mixture models. A construction of the DDP built on a Poisson point process. The concept is named after Peter Gustav Lejeune Dirichlet.

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  • In the mathematical theory of probability, the dependent Dirichlet process (DDP) provides a non-parametric prior over evolving mixture models. A construction of the DDP built on a Poisson point process. The concept is named after Peter Gustav Lejeune Dirichlet. In many applications we want to model a collection of distributions such as the one used to represent temporal and spatial stochastic processes. The Dirichlet process assumes that observations are exchangeable and therefore the data points have no inherent ordering that influences their labeling. This assumption is invalid for modelling temporal and spatial processes in which the order of data points plays a critical role in creating meaningful clusters. (en)
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  • In the mathematical theory of probability, the dependent Dirichlet process (DDP) provides a non-parametric prior over evolving mixture models. A construction of the DDP built on a Poisson point process. The concept is named after Peter Gustav Lejeune Dirichlet. (en)
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  • Dependent Dirichlet process (en)
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