Bayesian hierarchical model with Dirichlet process learns latent heterogeneity in innovation rates of count time series and shows favorable forecasting on Pittsburgh crime data.
The calculation of posterior distributions by data augmen- tation
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Learning a latent pattern of heterogeneity in the innovation rates of a time series of counts
Bayesian hierarchical model with Dirichlet process learns latent heterogeneity in innovation rates of count time series and shows favorable forecasting on Pittsburgh crime data.