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

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 languageEnglish
Title of host publicationContemporary Challenges and Solutions in Applied Artificial Intelligence
Pages163-168
Number of pages6
Volume489
DOIs
Publication statusPublished - 2013

Publication series

NameStudies in Computational Intelligence
Volume489
ISSN (Print)1860949X

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