Analysing Complex Life Sequence Data with Hidden Markov Modelling

By Satu Helske, Jouni Helske and Mervi Eerola in Hidden Markov models

January 1, 2016

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Abstract

When analysing complex sequence data with multiple channels (dimensions) and long observation sequences, describing and visualizing the data can be a challenge. Hidden Markov models (HMMs) and their mixtures (MHMMs) offer a probabilistic model-based framework where the information in such data can be compressed into hidden states (general life stages) and clusters (general patterns in life courses). We studied two different approaches to analysing clustered life sequence data with sequence analysis (SA) and hidden Markov modelling. In the first approach we used SA clusters as fixed and estimated HMMs separately for each group. In the second approach we treated SA clusters as suggestive and used them as a starting point for the estimation of MHMMs. Even though the MHMM approach has advantages, we found it to be unfeasible in this type of complex setting. Instead, using separate HMMs for SA clusters was useful for finding and describing patterns in life courses.

Posted on:
January 1, 2016
Length:
1 minute read, 152 words
Categories:
Hidden Markov models
Tags:
Life course data
See Also:
From sequences to variables - Rethinking the relationship between sequences and outcomes
Combining Sequence Analysis and Hidden Markov Models in the Analysis of Complex Life Sequence Data
Minimum description length based hidden Markov model clustering for life sequence analysis