Statistics Seminar - Vianey Leos Barajas - Spatially-coupled hidden Markov models


Title: Spatially-coupled hidden Markov models

Speaker: Vianey Leos Barajas (University of  Toronto)

Abstract: Hidden Markov models (HMMs) provide a flexible framework to model time series data where the observation process,Y, is taken to be driven by an underlying latent state process, Z. In this talk, we will focus on discrete-time, finite-state HMMs as they provide a flexible framework that facilitates extending the basic structure in many interesting ways.

HMMs can accommodate multivariate processes by (i) assuming that a single state governs theM observations at time t, (ii) assuming that each observation process is governed by its own HMM, irrespective of what occurs elsewhere, or (iii) a balance between the two, as in the coupled HMM framework. Coupled HMMs assume that a collection of M observation processes is governed by its respective M state processes. However, the mth state process at timet, Z[m,t] not only depends on Z[m,t-1] but also on the collection of state processes Z[-m,t-1]. We introduce spatially-coupled hidden Markov models whereby the state processes interact according to an imposed neighborhood structure and the observations are collected across N spatial locations. We outline an application to short-term forecasting of wind speed using data collected across meteorological stations.

Date/Time: Tuesday November 30, 2021, 3:30 - 4:30 

Location: Virtual 

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Meeting ID: 971 9900 3250
Passcode: 643951
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