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.
| Language | English |
|---|---|
| Title of host publication | Contemporary Challenges and Solutions in Applied Artificial Intelligence |
| Pages | 163-168 |
| Number of pages | 6 |
| Volume | 489 |
| DOIs | |
| Publication status | Published - 2013 |
Publication series
| Name | Studies in Computational Intelligence |
|---|---|
| Volume | 489 |
| ISSN (Print) | 1860949X |
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Estimating mental states of a depressed person with bayesian networks. / Klein, Michel C.A.; Modena, Gabriele.
Contemporary Challenges and Solutions in Applied Artificial Intelligence. Vol. 489 2013. p. 163-168 (Studies in Computational Intelligence; Vol. 489).Research output: Chapter in Book / Report / Conference proceeding › Chapter › Academic › peer-review
TY - CHAP
T1 - Estimating mental states of a depressed person with bayesian networks
AU - Klein, Michel C.A.
AU - Modena, Gabriele
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84883683522&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84883683522&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-00651-2_22
DO - 10.1007/978-3-319-00651-2_22
M3 - Chapter
SN - 9783319006505
VL - 489
T3 - Studies in Computational Intelligence
SP - 163
EP - 168
BT - Contemporary Challenges and Solutions in Applied Artificial Intelligence
ER -