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In this paper, we propose an improved combined forecasting model integrates the merits of data pretreatment, combined model and Markov chain, known as Markov combined model. The moving average is used for the data pretreatment or determination of trend, combined model is designed for the trend forecasting, and the Markov chain is employed for modifying the forecasting results of combined model. The...
The task of finding transcription start sites (TSSs) can be modeled as a classification problem. Relevance vector machines (RVM) is a family of machine learning methods that represent a Bayesian approach to the training of general linear models (GLM). Based on the Markov-chain Monte Carlo(MCMC) sampler, propose a model for using the RVM to explore very large numbers of candidate features. The model...
Most agricultural, hydrological and ecological models require daily rainfall data to run. However, such data at many sites are often incomplete or simply unavailable. In this study we developed a single-site daily rainfall generator for the simulations of rainfall occurrences and amounts in arid and semi-arid areas in the Middle East. A first-order two-state Markov chain was used to determine the...
China has already become world's largest coal producer and consumer. China's production in 2006 roughly equaled the combined production of the next four top producers (the United States, India, Australia and Russia). The dynamic GM(1,1) model of grey theory is used to forecast the coal production and consumption in China. In order to improve the forecast accuracy, the original GM(1, 1) models are...
The present financial tsunami has brought credit risk into a main focus. Dynamic credit scoring tools is highly demanded by commercial banks with products like credit cards. However, till now practitioners almost only employ statistical scoring models such as regressions. Thus the purpose of this paper is to provide for a new direction of attempts in modeling consumer credit risk and behavioral scoring...
In order to improve the forecasting precision of the electric-power demand, a Grey-Markov model for forecasting the electric-power demand is established. The model combines the advantages of both Grey forecasting method and Markov chains forecasting method, overcomes the influence of random fluctuation data on forecasting precision and widens the application scope of the Grey forecasting. An application...
We propose a shape population metric that reflects the interdependencies between points observed in a set of examples. It provides a notion of topology for shape and appearance models that represents the behavior of individual observations in a metric space, in which distances between points correspond to their joint modeling properties. A Markov chain is learnt using the description lengths of models...
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