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Subject Item
dbr:Dependent_Dirichlet_process
rdfs:label
Dependent Dirichlet process
rdfs:comment
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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dbc:Stochastic_processes dbc:Nonparametric_statistics dbc:Bayesian_statistics
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54336550
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1099620602
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dbr:Poisson_point_process dbr:Cluster_analysis dbr:Exchangeable_random_variables dbr:Mixture_model dbr:Probability dbr:Dirichlet_process dbc:Stochastic_processes dbr:Prior_probability_distribution dbc:Nonparametric_statistics dbr:Peter_Gustav_Lejeune_Dirichlet dbr:Stochastic_process dbc:Bayesian_statistics
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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.
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