Bayesian Nonparametric Inference: Dependence Structures and their Applications (17w5060)

The Casa Matemática Oaxaca (CMO) will host the "Bayesian Nonparametric Inference: Dependence Structures and their Applications" workshop from December 3rd to December 8th, 2017.

One of the current research frontiers in Bayesian statistics is inference for
random distributions and random functions -- in general random elements
in infinite dimensional spaces. Such statistical scheme is known as
nonparametric Bayesian (BNP) inference. BNP is a relatively young area
of research and its popularity continues to dramatically increase, mainly
because it naturally allows for rich and flexible probabilistic modeling in
setups such as nonparametric regression, survival regression, random
partitions, feature allocation models etc. Such problems often arise in
modern data analysis problems, including internet data, high throughput
genomic experiments, data mining with large observational data bases,
and in inference for big data.

One of the critical gaps in this literature are methods to address complex
forms of dependence. The objective of this conference is to fill this gap.
We have chosen to meet at Casa Matemática Oaxaca to build on an
existing strong tradition of related research in Mexico (UNAM, ITAM and
CIMAT).

The Casa Matemática Oaxaca (CMO) in Mexico, and the Banff International Research Station for Mathematical Innovation and Discovery (BIRS) in Banff, are collaborative Canada-US-Mexico ventures that provide an environment for creative interaction as well as the exchange of ideas, knowledge, and methods within the Mathematical Sciences, with related disciplines and with industry.

The research station in Banff is supported by Canada's Natural Science and Engineering Research Council (NSERC), the U.S. National Science Foundation (NSF), Alberta's Advanced Education and Technology, and Mexico's Consejo Nacional de Ciencia y Tecnología (CONACYT). The research station in Oaxaca is funded by CONACYT.