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Activity Number:
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635
- Advances in Machine Learning
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Type:
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Contributed
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Date/Time:
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Thursday, August 2, 2018 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistical Learning and Data Science
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Abstract #330663
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Title:
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An Approximation to the Information Matrix of Hidden Markov Model
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Author(s):
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Qing Ji* and Andrew Raim and Nagaraj Neerchal
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Companies:
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University of Maryland, Baltimore County and U.S. Census Bureau and University of Maryland, Baltimore County
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Keywords:
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Hidden Markov Model;
Information Matrix
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Abstract:
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A hidden Markov model depends on an unobserved Markov process which determines the distribution of the observed outcome over time. The Fisher information matrix is often used to compute standard errors in likelihood-based inference, but a closed form of the matrix is not available for hidden Markov models. In this work, we propose an approximation to the FIM based on the join probability density function of response variable and the unobserved Markov process, and show it converges to the true information matrix under certain conditions.
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Authors who are presenting talks have a * after their name.
