DocumentCode
1842341
Title
Bayesian neural networks with correlating residuals
Author
Vehtari, Aki ; Lampinen, Jouko
Author_Institution
Lab. of Comput. Eng., Helsinki Univ. of Technol., Espoo, Finland
Volume
3
fYear
1999
fDate
1999
Firstpage
1662
Abstract
In a multivariate regression problem it is often assumed that residuals of outputs are independent of each other. In many applications a more realistic model would allow dependencies between the outputs. In this paper we show how a Bayesian treatment using the Markov chain Monte Carlo method can allow for a full covariance matrix with multilayer perceptron neural network
Keywords
Markov processes; Monte Carlo methods; covariance matrices; multilayer perceptrons; statistical analysis; Bayesian neural networks; Markov chain; Monte Carlo method; covariance matrix; multilayer perceptron; multivariate regression; residuals; Additive noise; Bayesian methods; Covariance matrix; Gaussian noise; Input variables; Laboratories; Monte Carlo methods; Multivariate regression; Neural networks; Noise level;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location
Washington, DC
ISSN
1098-7576
Print_ISBN
0-7803-5529-6
Type
conf
DOI
10.1109/IJCNN.1999.832623
Filename
832623
Link To Document