From the 1 of 7 linked papers with an AI index.
7 papers
The Dirichlet Process as sampling distribution
Luis E. Nieto-Barajas
The paper treats the Dirichlet process as a data‑generating model and develops Bayesian inference for its centering measure and precision parameter, demonstrating the approach with…
Modelling heavy tail data with bayesian nonparametric mixtures
Luis E. Nieto-Barajas
In the study of heavy tail data, several models have been introduced. If the interest is in the tail of the distribution, block maxima or excess over thresholds are the typical app…
Leaf clustering using circular densities
Luis E. Nieto-Barajas
In the biology field of botany, leaf shape recognition is an important task. One way of characterising the leaf shape is through the centroid contour distances (CCD). Each CCD path…
Negative binomial models for development triangles of counts
Luis E. Nieto-Barajas, Rodrigo S. Targino
Prediction of outstanding claims has been done via nonparametric models (chain ladder), semiparametric models (overdispersed poisson) or fully parametric models. In this paper, we…
Graphical models with marginals in the exponential family
Luis E. Nieto-Barajas, Simón Lunagómez
Graphical models encode conditional independence statements of a multivariate distribution via a graph. Traditionally, the marginal distributions in a graphical model are assumed t…
Bayesian nonparametric mixtures of Archimedean copulas
Ruyi Pan, Luis E. Nieto-Barajas, Radu V. Craiu
Copula-based dependence modeling often relies on parametric formulations. This is mathematically convenient, but can be statistically inefficient when the parametric families are n…