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

- Calendar
- Mathematics & Statistics

- Date
- 11.30.2021 3:30 pm - 4:30 pm

### Description

**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 the

*M*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 time

*t*, 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

https://mcmaster.zoom.us/j/97199003250?pwd=dTVzUW5YaWovRm5GMEpwanpxT2JuZz09

Meeting ID: 971 9900 3250

Passcode: 643951