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First, we import the PyMC and NumPy namespaces:ģ.2.1. We can represent model disaster_model in a file calledĭisaster_model.py (the actual file can be found in pymc/examples/) asįollows. These objects might be RandomEvenGivenValuesOfParents. Stochastic class represents these variables. Plausible their candidate values are, given values for their parents. Variables are characterized by probability distributions that express how Late_mean were known, we would still be uncertain of their values. Switchpoint, disasters (before observing the data), early_mean or On the other hand, even if the values of the parents of variables This class would be DeterminedByValuesOfParents. A more descriptive (though more awkward) name for Represent random variables since the parents of \(r\) are random, The nomenclature is a bit confusing, because these objects usually Mathematical function that returns its value given values for its parents.ĭeterministic variables are sometimes called the systemic part of the A Deterministic like \(r\) is defined by a
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The only Deterministic in the model is \(r\). Random variables are represented in PyMC by the Values of \(x\) for which \(p(x)\) is high are relatively more Our knowledge and uncertainty about \(x\)‘s value. Random variable \(x\)‘s probability distribution \(p(x)\) represents The Bayesian interpretation of probability is epistemic, meaning \(s\), \(e\), \(r\) and \(l\) are all random variables.īayesian “random” variables have not necessarily arisen from a physical random At the model-specification stage (before the data are observed), \(D\),
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