Autor:
Ragnar Vutt

Tartu Workshop on Markov Modelling

Since the success of hidden Markov models (HMM) in the last decades of the 20th century, HMM-related modeling has been rapidly evolving. Often HMMs is a too limited model class, thus there is an increasing interest towards more general, flexible and complex models where the HMM-like dynamic programming algorithms are still applicable. One such surprisingly rich class of stochastic models is the so-called multiple (multidimensional) Markov models, including pairwise and triplet Markov models. A marginal or observed process of such a multiple model might lack the Markov property itself (allowing modeling the long dependence), but the joint Markov property makes dynamic programming algorithms often work.

The Tartu Workshop on Markov Modeling (28-29 November 2024, Tartu, Estonia), will bring together experts in different areas of Markov modeling. The topics of the workshop cover both theory and applications: Markov models, hidden (semi)-Markov models, asymptotics, dynamic programming, applications of Markov models in genetics, statistical inference, parameter estimation, ergodic theory, segmentation, evolution models, applications of Markov models in genetics, Bayesian approach, sequence comparison.

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