5 papers
Exact two-stage finite-mixture representations for species sampling processes
Ramsés H. Mena, Christos Merkatas, Theodoros Nicoleris +1
Discrete random probability measures are central to Bayesian inference, particularly as priors for mixture modeling and clustering. A broad and unifying class is that of proper spe…
Markov Stick-breaking Processes
MarÃa F. Gil-Leyva, Antonio Lijoi, Ramsés H. Mena +1
Stick-breaking has a long history and is one of the most popular procedures for constructing random discrete distributions in Statistics and Machine Learning. In particular, due to…
Weighted Support Points from Random Measures: An Interpretable Alternative for Generative Modeling
Peiqi Zhao, Carlos E. RodrÃguez, Ramsés H. Mena +1
Support points summarize a large dataset through a smaller set of representative points that can be used for data operations, such as Monte Carlo integration, without requiring acc…
Reconstruction of the Probability Measure and the Coupling Parameters in a Curie-Weiss Model
Miguel Ballesteros, Ramsés H. Mena, Arno Siri-Jégousse +1
The Curie-Weiss model is used to study phase transitions in statistical mechanics and has been the object of rigorous analysis in mathematical physics. We analyse the problem of re…
On a divergence-based prior analysis of stick-breaking processes
José A. PerusquÃa, Mario Diaz, Ramsés H. Mena
The nonparametric view of Bayesian inference has transformed statistics and many of its applications. The canonical Dirichlet process and other more general families of nonparametr…