# MSCS Seminar Calendar

Tuesday August 28, 2018

**Logic Seminar**

Strongly minimal Steiner systems

John Baldwin (UIC)

3:30 PM in 427 SEO

With Gianluca Paolini, we constructed families of strongly minimal Steiner
$(\infty,2,k)$ systems for every $k \geq 3$. Here we show that the
$2^{\aleph_0}$ Steiner $(2,3)$-systems are coordinatized by strongly
minimal Steiner quasigroups and the Steiner $(2,4)$-systems are
coordinatized by strongly minimal $SQS$-Skeins. Further the Steiner
$(2,4)$-systems admit Steiner quasigroups but it is open whether their
theory is strongly minimal. We exhibit strongly minimal uniform Steiner
triple systems (with respect to the associated graphs $G(a,b)$ (Cameron and
Webb) with varying numbers of finite cycles. This work inaugurates a
program of differentiating the many strongly minimal sets, whose geometries
of algebraically closed sets are (locally isomorphic) to the original
Hrushovski example, but with varying properties in the object language.

Wednesday August 29, 2018

**Statistics Seminar**

Distributions of pattern statistics in sparse Markov models

Donald E.K. Martin (North Carolina State University)

4:00 PM in 636 SEO

Higher-order Markov models provide a good approximation to probabilities associated with many categorical time series, and thus they are applied extensively. However, a major drawback associated with them is that the number of model parameters grows exponentially in the order of the model, and thus only very low-order models are considered in applications. Another drawback is lack of flexibility, in that higher-order Markov models give relatively few choices for the number of model parameters. Sparse Markov models are Markov models where transition probabilities are lumped into classes comprised of invariant probabilities. The contexts for conditioning may be either hierarchical (as in variable length Markov chains) or non-hierarchical. This supplies a model that helps with the two problems given above, and which thus gives a better handling of the trade-off between bias associated with having too few model parameters and variance associated with having too many. In this work, methods for efficient computation of pattern distributions through Markov chains with minimal state spaces are extended to the sparse Markov framework.

Thursday August 30, 2018

Wednesday September 5, 2018

Friday September 7, 2018

Wednesday September 12, 2018

Tuesday September 18, 2018

Wednesday September 19, 2018

Friday September 21, 2018

Wednesday September 26, 2018

Friday September 28, 2018

Monday October 1, 2018

Wednesday October 10, 2018

Monday October 29, 2018

Wednesday October 31, 2018

Friday November 2, 2018

Wednesday November 7, 2018

Friday November 9, 2018

Wednesday November 14, 2018

Friday November 30, 2018

Friday March 1, 2019