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Bayesian semi- and nonparametric modelling

The field of Bayesian nonparametrics has seen a remarkable growth over the last 15 years. It is concerned with placing priors on infinite dimensional objects such as unknown distributions (which may depend on covariates) and leads to flexible models and powerful estimation procedures. The methods have been used for problems such as density estimation, survival analysis, clustering, factor analysis and semiparametric modelling - often combined with Bayesian hierarchical modelling and approximate computational methods. This has lead to many applications in areas such as biology, economics, finance, medicine and machine learning.

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Raffaele Argiento, University of Torino, Italy
Li Ma, Duke University, United States
Matteo Ruggiero, University of Torino, Italy
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