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Exact(7)
4, we seek an approximation of the expectation term in (4).
The most common solution is the Monte Carlo approximation of the expectation (4).
As long as the number of samples is sufficiently large, we can get a good approximation of the expectation by (10).
This yields an approximation of the expectation of under the posterior, which is called the minimum mean-square error (MMSE) estimate.
Unbiasedness implies E{ϵ n }=0, and a first‐order approximation of the expectation of f n (x n−1) leads to the prior estimate x ̂ n − = f ̄ n ( x n − 1 ) = f n ( x ̂ n − 1 ).
Finally, the best state at time t, x ̃ t, is derived based on the discrete approximation of Equation 3. The most common solution is the Monte Carlo approximation of the expectation as x ̃ t = E [ x t | z 1 : t ] ≈ ∑ j = 1 N P w t j x t j. (6).
Similar(53)
Averaging over this few number of symbols is not a good approximation for the expectation of the Gardner error over a segment.
The numerical approximations of the expectation (blue curves) stay well within the theoretical bounds (red curves), whereas single trajectories (purple curves) of course may exceed the bounds of the average.
Closed form approximation to the expectation is developed and is numerically shown to agree well with the exact models.
However, by utilizing the approximation of the conditional expectation of the concentration, we can still use a proportional-like system noise.
In the SDE above, Ĉ i denotes the approximation of the conditional expectation of the concentration C i computed by the EKF.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com