Bayesian Approach

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Given a set of forecasts from different methods, we regard the values as sample points from a density. Since at each time we only have a few forecast values, therefore a classical approach for mode estimate seems to be inappropriate. A Bayesian approach is therefore considered. Here we assume that the forecast values do not follow a Gaussian distribution and we assume that it follows the Weibull distribution. This assumption is supported by the empirical evidence. The Weibull distribution is a commonly used distribution in many applications when the density is assumed to be non-Gaussian (see Kotz and Johnson [1988] and the references therein).

We say that a random variable X follows the two-parameter Weibull distribution, denoted as We(a,b), if the density function and the cumulative distribution function are:



The log-likelihood function is given by


The parameter b(b > 0) is a dimensionless shape parameter and a(a > 0) is a scale parameter with the same dimension as X. For a Bayesian approach, we need to choose a joint prior distribution for parameter a and b. As we lack information about this density, we choose the joint prior distribution inversely proportional to the scale parameter a. That is, the joint prior distribution . Then the joint posterior is


It can be shown that the joint posterior distribution and the marginal posterior distribution a and b are proper posterior densities. Therefore important statistics about the parameters such as the posterior mode can be obtained by using the proper joint posterior distribution in equation (17).

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Managing Data Mining Technologies in Organizations(c) Techniques and Applications
Managing Data Mining Technologies in Organizations: Techniques and Applications
ISBN: 1591400570
EAN: 2147483647
Year: 2003
Pages: 174 © 2008-2017.
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