Seminar on Markov Models
Completed
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 | Â | Â | Topic | Â | Â | Speaker |
|---|---|---|---|---|---|---|
| 20.02.2024 | Â | Â | Introduction | Â | Â | S. Koovely |
| 05.03.2024 | Â | Â | Discrete-Time Markov Chains | Â | Â | L. J. Bentele |
| 12.03.2024 | Â | Â | Hidden Markov Models | Â | Â | L. Siciliano |
| 19.03.2024 | Â | Â | Markov Chain Monte Carlo | Â | Â | N. Hai |
| 26.03.2024 | Â | Â | Continuous-Time Markov Chains | Â | Â | A. Mokhova |
| 09.04.2024 | Â | Â | Undirected Graphical Models | Â | Â | M. Jaquier |
| 16.04.2024 | Â | Â | Chow-Liu Trees | Â | Â | C. Barzasi |
| 23.04.2024 | Â | Â | Bayesian Networks | Â | Â | L. Ramsauer |
| 30.04.2024 | Â | Â | Causal Inference | Â | Â | 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