Data and Likelihood
In BayesBay, observed data should be embedded in a Target. When paired with a forward function that enables data predictions from the considered model parameters, the Target instance can be utilized to define an instance of LogLikelihood. Importantly, Target facilitates treating the noise properties of the associated data as unknown, enabling hierarchical inversions.
An instance of LogLikelihood can encapsulate multiple Target instances, each associated with a different forward function. This capability facilitates the joint inversion of various types of data sets.
Observed data with noise that can be treated as an unknown |
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Helper class to evaluate the log likelihood ratio |