DS and inverse problems

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Jan 162011

Suppose dY(t) = A\theta(t)dt + \epsilon d W(t) and let (\psi_i) be a basis (frame?) for space to which \theta belongs.
Act this observation functional on \psi_i, then we get a sequence space representation as Y_i = x_i + \epsilon \kappa_i^{-1} W_i where (W_i) forms a non-independent, but nearly independent set if A is sufficiently well behaved (think homogeneous, dilation invariant; or polynomial decay convolution).

  1. Ask James about his DS for correlated noise
  2. We can apply this to WVD by the above formulation.
  3. Fundamental Question:  Can we use the adaptive sampling framework to estimate $\latex \theta$ better?
Posted by Darren Warren at 1:04 pm
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