Asset Details
Do you wish to reserve the book?
Bayesian modeling via discrete nonparametric priors
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Bayesian modeling via discrete nonparametric priors
Do you wish to request the book?
Bayesian modeling via discrete nonparametric priors
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Bayesian modeling via discrete nonparametric priors
2023
Overview
The availability of complex-structured data has sparked new research directions in statistics and machine learning. Bayesian nonparametrics is at the forefront of this trend thanks to two crucial features: its coherent probabilistic framework, which naturally leads to principled prediction and uncertainty quantification, and its infinite-dimensionality, which exempts from parametric restrictions and ensures full modeling flexibility. In this paper, we provide a concise overview of Bayesian nonparametrics starting from its foundations and the Dirichlet process, the most popular nonparametric prior. We describe the use of the Dirichlet process in species discovery, density estimation, and clustering problems. Among the many generalizations of the Dirichlet process proposed in the literature, we single out the Pitman–Yor process, and compare it to the Dirichlet process. Their different features are showcased with real-data illustrations. Finally, we consider more complex data structures, which require dependent versions of these models. One of the most effective strategies to achieve this goal is represented by hierarchical constructions. We highlight the role of the dependence structure in the borrowing of information and illustrate its effectiveness on unbalanced datasets.
Publisher
Springer Nature Singapore,Springer Nature B.V
