Model Multinomial Bayesian Network pada Data Simulasi Curah Hujan - Nanda Arista Rizki; Syaripuddin Syaripuddin; Sri Wahyuningsih (Program Studi Statistika Unisba, 2012)
Bayesian Networks is one of simple Probabilistic Graphical Models are built from theory of bayesprobability and graph theory. Probability theory Is directly related to data while graph theory directlyrelated to the form representation to be obtained. Multinomial Bayesian Network method is onemethod that involves the influence of spatial linkages suggest a link between rainfall observationstations. The objective of this study was seek the result of the model probabilistic a graphMultinomial Bayesian Network and apply it in forecasting with Oldeman classification based on oneor two rainfall stations are known. This research uses simulated data for 14 stations respectively each300 sets of data. The data generated is normal distribution of data based on parameters that havebeen determined and classified using the classification Oldeman. Bayesian Network structureconstructed using the K2 algorithm. Markov chain transition matrix is formed based on the Bayesianof the nodes are directional. Model of Multinomial Bayesian Network was established based onMarkov transition matrices. The result of probability model can predict the probability of rainfall insome stations based on one or two rainfall stations are known, which is a model graph with 14 nodesand 13 arcs.