Regression using hybrid Bayesian networks: modelling landscape - socioeconomy relationships
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Fernández Ropero, Rosa María; Aguilera Aguilera, Pedro; Fernández Álvarez, Antonio; Rumí Rodríguez, RafaelDate
2014Abstract
Modelling environmental systems becomes a challenge when dealing directly with continuous and discrete data simultaneously. The aim in regression is to give a prediction of a response variable given the value of some feature variables. Multiple linear regression models, commonly used in environmental science, have a number of limitations: (1) all feature variables must be instantiated to obtain a prediction, and (2) the inclusion of categorical
variables usually yields more complicated models. Hybrid Bayesian networks are an appropriate approach to solve regression problems without such limitations, and they also provide additional advantages. This methodology
is applied to modelling landscape - socioeconomy relationships for different types of data (continuous, discrete or hybrid). Three models relating socioeconomy and landscape are proposed, and two scenarios of socioeconomic
change are introduced in each one to obtain a prediction. This proposal can be easily applied to other a...
Palabra/s clave
Continuous Bayesian Networks
Mixtures of Truncated Exponentials
Regression, Landscape
Socioeconomic Structure