Parameter identification in a probabilistic setting

The parameters to be identified are described as random variables, the randomness reflecting the uncertainty about the true values, allowing the incorporation of new information through Bayes’s theorem. Such a description has two constituents, the measurable function or random variable, and the probability measure. One group of methods updates the measure, the other group changes the function. We connect both with methods of spectral representation of stochastic problems, and introduce a computational procedure without any sampling which works completely deterministically, and is fast and reliable. Some examples we show have highly nonlinear and non-smooth behaviour and use non-Gaussian measures.

► Surveys parameter identification as well-posed stochastic problem.
► Shows connection and use of both Bayes’s formula and conditional expectation.
► Connects linear approximation to conditional expectation with Kalman filter.
► Shows computational procedures with spectral approximation of stochastics.