*Tid:***30 oktober 2000 kl 1515-1700 **

*Plats :***Seminarierummet 3733**, Institutionen för
matematik, KTH, Lindstedts väg 25, plan 7. Karta!

*Föredragshållare:***
Gunnar
Englund, Matematisk
statistik, KTH**.

**Titel:** **
Markov Chain Monte Carlo, contingency tables and Gröbner bases
**

* Sammanfattning: *
Markov
Chain Monte Carlo (MCMC) is a powerful technique to
simulate from complicated distributions which has been used extensively
in e g Bayesian
analysis. MCMC can
also be used to analyse
e.g. contingency
tables and logistic
regression or more generally
conditional distributions given restrictions (sufficient statistics)
by simulation without having to resort to asymptotic
-distributions. A simple Markov chain
is constructed which has
the desired distribution as a stationary
distribution. Gröbner bases
can be used to ensure that the resulting Markov chain is irreducible
and aperiodic and hence ergodic.

The talk is based on the article "Algebraic algorithms for sampling from conditional distributions" by Persi Diaconis and Bernd Sturmfels in Annals of Statistics 1998 (Vol. 26).

Preliminary version of overheads (English) or Swedish.