Analysis And Comparison Of Bayesian Learning Algorithms On Meteorological Databases

In this paper, we introduce bayesian artificial networks as a causal modeling tool And analyse bayesian learning algorithms. Two important methods of learning bayesian are parameter learning and structure learning. Because of its impact on inference and forecasting results, Learning algorithm selection process in bayesian network is very important. As a first step, key learning algorithms, like Naïve Bayes Classifier, Hill Climbing, K2, LK2, Greedy Thick Thinning are implemented and Are compared based on accuracy and structured network time.. We work with a database of observations (monthly rainfall) measured for the years 1985-2010 in a network of 22 stations in the (Razavi, Shomali And Jonoubi) Khorasan provinces and with the corresponding gridded atmospheric patterns generated by a numerical circulation model. Finally, the best of learning algorithm will be proposed

Keywords:

Bayesian Network , Learning Algorithms , Structure Learning , Parametr Learning , Meteorological Databases