Abstract
In this work in progress paper we present an approach based on Bayesian Networks to model the relationship between mental states and empirical observations in a depressed person. We encode relationships and domain expertise as a Hierarchical Bayesian Network. Mental states are represented as latent (hidden) variables and the measurements found in the data are encoded as a probability distribution generated by such latent variables; we provide examples of how the network can be used to estimate mental states.
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Klein, M.C.A., Modena, G. (2013). Estimating Mental States of a Depressed Person with Bayesian Networks. In: Ali, M., Bosse, T., Hindriks, K., Hoogendoorn, M., Jonker, C., Treur, J. (eds) Contemporary Challenges and Solutions in Applied Artificial Intelligence. Studies in Computational Intelligence, vol 489. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00651-2_22
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DOI: https://doi.org/10.1007/978-3-319-00651-2_22
Publisher Name: Springer, Heidelberg
Print ISBN: 978-3-319-00650-5
Online ISBN: 978-3-319-00651-2
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