Estimating mental states of a depressed person with bayesian networks

Michel C.A. Klein, Gabriele Modena

Research output: Chapter in Book / Report / Conference proceedingChapterAcademicpeer-review


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.

Original languageEnglish
Title of host publicationContemporary Challenges and Solutions in Applied Artificial Intelligence
Number of pages6
Publication statusPublished - 2013

Publication series

NameStudies in Computational Intelligence
ISSN (Print)1860949X


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