Seminar on Markov Models


Markov models are a foundational concept in probability theory and offer insights into systems that evolve over time, making them vital in diverse fields like statistics, computer science, biology, and economics.
This seminar consists of an exploration of a wide array of Markov models and their applications, including discrete-time and continuous-time Markov chains, Bayesian networks, Markov random fields, hidden Markov models, Markov decision processes, and Markov Chain Monte Carlo methods.

The plan of the talks goes as follows:

Date     Topic     Speaker
20.02.2024     Introduction     S. Koovely
05.03.2024     Discrete-Time Markov Chains     L. J. Bentele
12.03.2024     Hidden Markov Models     L. Siciliano
19.03.2024     Markov Chain Monte Carlo Methods     N. Hai
26.03.2024     Continuous-Time Markov Chains     A. Mokhova
09.04.2024     Undirected Graphical Models     M. Jaquier
16.04.2024     Chow-Liu Trees     C. Barzasi
23.04.2024     Bayesian Networks     L. Ramsauer
30.04.2024     Causality     F. Grifone
07.05.2024     The Wiener Process     D. Dell’Angelo

You can find it as well here , together with the references for the material covered during the talks.

Reading Assignments:
Durret - Probability: Theory and Examples: Th. 5.2.1, Th. 5.2.2, and Th. 5.2.3
Daniel Jurafsky and James H Martin - Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and SpeechRecognition: Appendix A.1 and A.2