26 04 im alumniV8
22 11 im fatiado face
22 11 im fatiado twitter
22 11 im fatiado youtube
22 11 im fatiado gmail
22 11 im fatiado brazil
22 11 im fatiado england
22 11 im fatiado spain

13 09 Noticia ColoquioTítulo: Sparse Markov models for high-dimensional inference

Palestrante: Guilherme Ost (Instituto de Matemática, UFRJ)
Data: 16/09/2022
Horário: 15:00h
Local: Sala C-116

Resumo: Consider a sample of size n of a finite order Markov chain. In this full generality, we can only estimate the parameters of the Markov chain (the order d and the transition probabilities) in the regime d = O(log(n)), limiting the practical application of these chains to small orders only. In this talk, we will discuss a way to overcome this constraint in a large subclass of Markov chains, namely the Mixture of Transition Distribution (MTD) models. In our main result, we will show that it is possible to select a priori the portion of the past that is relevant for the transition probabilities of a MTD, allowing the estimation of the model parameters even in the regime d = O(n). The practical performance of our estimation procedure will be illustrated through simulations. This is a joint work with Daniel Takahashi (Brain Institute - UFRN).

Sparse Markov models for high-dimensional inference