2012/07/11 by Sergey Kirshner, Padhraic Smyth, Kirshner, Sergey +4
Computer Science · Environmental Science · Mathematics · #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Hydrological Forecasting Using AI #Hydrology and Drought Analysis #Machine Learning (cs.LG) #Machine Learning (stat.ML) #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1207.4142
Appears in Proceedings of the Twentieth Conference on Uncertainty in Artificial Intelligence (UAI2004)
arxiv created 2012/07/11 · openalex publication_date 2012/07/11 · arxiv updated 2012/07/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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. Conditional Chow-Liu tree models are introduced, as 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. These models are applied to the important problem of simulating and forecasting daily precipitation occurrence for networks of rain stations. To demonstrate 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.