Buscar
Mostrando ítems 31-40 de 46
Theory and applicatións of dependent nonparametric bayesian models for bounded and unbounded responses
(2012)
The definition and study of theoretical properties of probability models defined on infinite - dimensional spaces have received increasing attention in the statisticalliterature because these models are the basis for the ...
Fully nonparametric regression for bounded data using dependent bernstein polynomials
(2017)
We propose a novel class of probability models for sets of predictor-dependent probability distributions with bounded domain. The proposal extends the DirichletBernstein prior for single density estimation, by using dependent ...
Fully nonparametric regression for bounded data using dependent bernstein polynomials
(2017)
We propose a novel class of probability models for sets of predictor-dependent probability distributions with bounded domain. The proposal extends the DirichletBernstein prior for single density estimation, by using dependent ...
Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection
This open access book focuses on robot introspection, which has a direct impact on physical human–robot interaction and long-term autonomy, and which can benefit from autonomous anomaly monitoring and diagnosis, as well ...
Optimal sampling for repeated binary measurements
(CANADIAN JOURNAL STATISTICS, 2004)
The authors consider the optimal design of sampling schedules for binary sequence data. They propose an approach which allows a variety of goals to be reflected in the utility function by including deterministic sampling ...
Learning latent jet structure
(Multidisciplinary Digital Publishing Institute, 2021-06-29)
We summarize our recent work on how to infer on jet formation processes directly from substructure data using generative statistical models. We recount in detail how to cast jet substructure observables’ measurements in ...
DPpackage: Bayesian Semi- and Nonparametric Modeling in R
(JOURNAL STATISTICAL SOFTWARE, 2011)
Data analysis sometimes requires the relaxation of parametric assumptions in order to gain modeling flexibility and robustness against mis-specification of the probability model. In the Bayesian context, this is accomplished ...