Kirshner Sergey author Smyth Padhraic author Robertson Andrew W. author Columbia University. International Research Institute for Climate and Society Columbia University. International Research Institute for Climate and Society originator text Technical reports Irvine, Calif. School of Information and Computer Science, University of California, Irvine 2004 We consider the problem of modeling discrete-valued vector time series data using extensions of Chow-Liu tree models to capture both dependencies across time and dependencies across variables. We introduce conditional Chow-Liu tree models, an extension to standard Chow-Liu trees, for modeling conditional rather than joint densities. We describe learning algorithms for such models and show how they can be used to learn parsimonious representations for the output distributions in hidden Markov models. We illustrate how these models can be applied to the important problem of simulating and forecasting daily precipitation occurrence for networks of rain stations. To illustrate the effectiveness of the models, we compare their performance versus a number of alternatives using historical precipitation data from Southwestern Australia and the Western United States. We illustrate how the structure and parameters of the models can be used to provide an improved meteorological interpretation of such data. Meteorology 04-04 http://hdl.handle.net/10022/AC:P:14472 English NNC NNC 2012-08-21 15:23:43 -0400 2012-08-21 15:39:50 -0400 8492 eng