<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>0379-3982</journal-id>
<journal-title><![CDATA[Revista Tecnología en Marcha]]></journal-title>
<abbrev-journal-title><![CDATA[Tecnología en Marcha]]></abbrev-journal-title>
<issn>0379-3982</issn>
<publisher>
<publisher-name><![CDATA[Instituto Tecnológico de Costa Rica]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0379-39822019000400158</article-id>
<article-id pub-id-type="doi">10.18845/tm.v32i4.4800</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Evaluación del uso de Redes Bayesianas Dinámicas para la predicción del avance de la Sigatoka negra y la productividad en cultivos agrícolas]]></article-title>
<article-title xml:lang="en"><![CDATA[Evaluation of Dynamic Bayesians Networks for predicting the progress of the Black Sigatoka and the productivity in crops]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Calvo-Valverde]]></surname>
<given-names><![CDATA[Luis Alexander]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Argüello]]></surname>
<given-names><![CDATA[Sebastián]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Guzmán-Alvarez]]></surname>
<given-names><![CDATA[José-Antonio]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Guzmán-Quesada]]></surname>
<given-names><![CDATA[Mauricio]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[González-Zúñiga]]></surname>
<given-names><![CDATA[Miguel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Instituto Tecnoló gico de Costa Rica Maestría en Computación. Programa Multidisciplinar eScience ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Costa Rica</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Instituto Tecnológico de Costa Rica Maestría en Computación ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Costa Rica</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Corporación Bananera Nacional S.A. (CORBANA  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Costa Rica</country>
</aff>
<aff id="Af4">
<institution><![CDATA[,Corporación Bananera Nacional S.A. (CORBANA  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Costa Rica</country>
</aff>
<aff id="Af5">
<institution><![CDATA[,Corporación Bananera Nacional S.A. (CORBANA  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Costa Rica</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2019</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2019</year>
</pub-date>
<volume>32</volume>
<numero>4</numero>
<fpage>158</fpage>
<lpage>170</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_arttext&amp;pid=S0379-39822019000400158&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_abstract&amp;pid=S0379-39822019000400158&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_pdf&amp;pid=S0379-39822019000400158&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Los Modelos Gráficos Probabilísticos (MGP) utilizan una representación basada en grafos para codificar de manera compacta distribuciones complejas en espacios de alta dimensionalidad. Un tipo de MGP son las Redes Bayesianas Dinámicas (RBDs) que se caracterizan por ser un sistema estacionario homogéneo, lo que permite que con ellas se pueden representar, de una manera compacta, grandes cantidades de información de muchas variables. En este trabajo se estudia la capacidad de predicción de las RBDs en cuanto al avance de la Sigatoka negra y la productividad del cultivo, utilizando los datos proporcionados por CORBANA. Estos datos tienen información histórica del clima y de dos fenómenos: el avance de la enfermedad denominada Sigatoka negra y la productividad del cultivo del banano. Para esto se comparó la capacidad de predicción de la RBDs con la de las Redes Bayesianas (RBs). Se diseñaron e implementaron una RBD y una RB que representan las relaciones encontradas en los datos, y con ellas se llevaron a cabo experimentos para identificar cómo los distintos factores inciden en la capacidad de predicción de las mismas. Los resultados obtenidos en los experimentos mostraron que la capacidad de predicción de las RBDs no supera la de las RBs utilizando los datos de la Corporación Nacional Bananera. De hecho, no se observó una diferencia significativa entre ambos tipos de red. Además, se observó gran diferencia en las ventajas teóricas del modelo de las RBDs frente a otros MGPs. Ya que en la práctica las limitaciones de las implementaciones disponibles hacen que no sea atractivo su uso.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract The Probabilistic Graphical Models (PGM) use a representation based on graphs to encode complex distributions in high dimensional spaces compactly. One type of PGM are the Dynamic Bayesian Networks (DBN) characterized for being a stationary and homogeneous system, allowing to represent huge amount of information of multiple variables in a compact way. In this paper the prediction capacity of the DBN on the evolution of the Black Sigatoka and the crops productivity, using the data from CORBANA is studied. This data contains historical information of the weather and of two phenomena: the evolution of the Black Sigatoka and the productivity of the crops. The prediction capacity of the DBN was compare with the Bayesian Networks (BN). A DBN and a BN were design and implemented representing the variables found on the data and their relations. Using them different experiments were done to determine the influence of the factors on their capacity of prediction. The obtained results on the experiments showed that the prediction capacity of the DBNs is not better that the prediction capacity of the BN using the data from CORBANA. In fact, there was not a significant difference when the network was changed. Although the DBN presented several theoretical advantages in comparison with other PGMs, in practice they were not observed. This happened because of the limitations of the available implementation of framework for using PGMs, making the DBNs not as attractive.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Redes Bayesianas Dinámicas]]></kwd>
<kwd lng="es"><![CDATA[Redes Bayesianas]]></kwd>
<kwd lng="es"><![CDATA[Modelos Gráficos Probabilísticos]]></kwd>
<kwd lng="en"><![CDATA[Dynamic Bayesian Networks]]></kwd>
<kwd lng="en"><![CDATA[Bayesian Networks]]></kwd>
<kwd lng="en"><![CDATA[Probabilistic graphical Models.]]></kwd>
</kwd-group>
</article-meta>
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