Bayesian data analysis

The goal of Bayesian or integrated data analysis (IDA) is to combine the information from a set of diagnostics providing complementary information in order to recover the best possible reconstruction of the actual state of the system subjected to measurement. Like Function parametrization (FP), this technique requires having a forward model to predict the measurement readings for any given state of the physical system; however

• instead of computing an estimate of the inverse of the forward model (as with FP), IDA 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 IDA, allowing non-Gaussian error distributions and absolutely general parameter interdependencies; and
• additionally, it provides a systematic way to include prior knowledge into the analysis.

See also

• Markov chain Monte Carlo