Estimating mental states of a depressed person with bayesian networks

Michel C.A. Klein; Gabriele Modena

doi:10.1007/978-3-319-00651-2_22

Estimating mental states of a depressed person with bayesian networks

Research output: Chapter in Book / Report / Conference proceeding › Chapter › Academic › peer-review

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.

Original language	English
Title of host publication	Contemporary Challenges and Solutions in Applied Artificial Intelligence
Pages	163-168
Number of pages	6
Volume	489
DOIs	https://doi.org/10.1007/978-3-319-00651-2_22
Publication status	Published - 2013

Publication series

Name	Studies in Computational Intelligence
Volume	489
ISSN (Print)	1860949X

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10.1007/978-3-319-00651-2_22

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