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doc/optimal.rst
@@ -129,7 +129,7 @@ The result of this optimization gives us the estimated state for the
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previous :math:`N` steps in time, including the "current" time
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:math:`x[N]`. The basic idea is thus to compute the state estimate that is
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most consistent with our model and penalize the noise and disturbances
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-according to how likely the are (based on a some sort of stochastic system
+according to how likely the are (based on the given stochastic system
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model for each).
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Given a solution to this fixed-horizon optimal estimation problem, we can
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