# Mathematics Colloquia and Seminars

In this talk we will consider the classical Polya-Eggenberger-Friedman Urn Model and a generalization with infinitely many colors and we will show that in the balanced case the configuration of the urn after $n$ steps can be obtained by sampling an associated Markov chain at a random time which depends on $n$. We will show that most of the classical results can be derived easily using this representation. Moreover we will show that new results can be derived for the infinite color case. If time permits then we will also discuss further generalization to urn models with negative reinforcement and show that the representation can be used to solve certain type of negative reinforcement urn models. [This is based joint work with Debleena Thacker and Gursharn Kaur].