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Bayesian data analysis: Difference between revisions
→Comparison with Function Parametrization
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[[Function parametrization]] (FP) is another statistical technique for recovering system parameters from diverse measurements. | [[Function parametrization]] (FP) is another statistical technique for recovering system parameters from diverse measurements. | ||
Both FP and Bayesian data analysis require having a ''forward model'' to predict the measurement readings for any given state of the physical system, and the state of the physical system and the measurement process is ''parametrized''. However | |||
* instead of computing an estimate of the inverse of the forward model (as with FP), Bayesian analysis finds the best model state corresponding to a specific measurement by a maximization procedure (maximization of the likelihood); | * instead of computing an estimate of the inverse of the forward model (as with FP), Bayesian analysis finds the best model state corresponding to a specific measurement by a maximization procedure (maximization of the likelihood); | ||
* the handling of error propagation is more sophisticated within Bayesian analysis, allowing non-Gaussian error distributions and absolutely general and complex parameter interdependencies; and | * the handling of error propagation is more sophisticated within Bayesian analysis, allowing non-Gaussian error distributions and absolutely general and complex parameter interdependencies; and | ||