Campuses:

Bayesian

Monday, April 23, 2018 - 9:10am - 10:00am
Chris Wikle (University of Missouri)
Spatio-temporal data are ubiquitous in engineering and the sciences, and their study is important for understanding and predicting a wide variety of processes. One of the chief difficulties in modeling spatial processes that change with time is the complexity of the dependence structures that must describe how such a process varies, and the presence of high-dimensional complex datasets and large prediction domains.
Wednesday, March 7, 2018 - 11:30am - 12:30pm
Adam Himes (Medtronic)
Medical device manufacturers are increasingly using predictive computer models, also called virtual patient models, that simulate clinical outcomes. In some cases, these virtual patient models can be incorporated into a study in a way that is analogous to how some Bayesian clinical trials incorporate historical data as prior information. Benefits of this approach may include increased information from the clinical study, more confidence in the clinical outcome, and in some cases smaller or shorter duration studies.
Wednesday, April 30, 2014 - 10:15am - 11:05am
Ulrich Wagner (IST Austria)
We discuss a number of open questions and results concerning
(from a combinatorialist's point of view) higher-dimensional
analogues of graph planarity and crossing numbers, i.e., embeddings
of finite simplicial complexes (compact polyhedra) into Euclidean space.

While embeddings are a classical topic in geometric topology, here
we focus rather on algorithmic and combinatorial aspects. Two typical
questions are the following:
(1) Is there an algorithm that, given as input a finite k-dimensional simplical
Tuesday, March 12, 2013 - 12:00pm - 12:30pm
Ralph Milliff (University of Colorado)
The Bayesian Hierarchical Model (BHM) methodology has been applied to the
generation of ensemble ocean surface vector winds in a sequence of increasingly
sophisticated models. This history is briefly reviewed to establish the
approach to BHM development in atmosphere-ocean contexts. Recently,
ensemble surface winds and wind stresses are obtained from BHMs, given data
stage inputs from satellites and weather-center analyses. Process model distributions
are based on leading
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