While all probability models follow either Bayesian or Frequentist inference, they can yield vastly different results depending upon what specific parameter distribution algorithm is employed. We assess the ability of the maximum entropy approach to extract relevant genetic relationships by analyzing microarray expression data from the well studied eukaryote Saccharomyces cerevisiae growing under conditions that the support energy metabolic oscillations (26, 27). However, under the maximum entropy principle, the researcher should go with whatever probability distribution they know the least about already.Ĭommon Probability Distribution Parameterizations in Machine Learning:
They might be tempted to choose a probability function like Normal distribution, since knowing the mean first lets them fill in more variables in the prior assumption. The maximum entropy method (MEM) and a similar technique called the maximum likelihood method are nonlinear or adaptive technique as their design is data independent. The goal is to maximize “uniformitiveness,” or uncertainty when making a prior probability assumption so that subjective bias is minimized in the model’s results.įor example, if only the mean of a certain parameter is known (the average outcome over long-term trials), then a researcher could use almost any probability distribution to build the model. The principle of maximum entropy is a model creation rule that requires selecting the most unpredictable (maximum entropy) prior assumption if only a single parameter is known about a probability distribution.
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