These so-called quantities of interest are characterized by linear functionals on the space of functions to where the solution belongs.
This will require using prior functional knowledge to calibrate a prior on the space of functions that places mass on a most likely subspace of functions.
Let denote the space of functions (1.4).
A more general example is the space of functions on an algebraic loop.
Next we introduce the space of functions with non-integer derivatives.
The study is conducted in the space of functions with bounded variations (BV).
Let be the space of functions, whose first derivative is absolutely continuous.
Let H∞ denote the space of functions in L∞(dθ2π) that have vanishing negative Fourier coefficients.
Here, the large rectangle represents the space of functions, with S, the image of the space of spike trains in the space of functions under the filter map represented by the wavy line.
Because S is a subset of the space of functions, it is inevitable that the image of the central spike train in the space of functions is different from the function average.
The spike trains are mapped into the space of functions by filtering them with a causal exponential filter.
