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Fig. 5 Expected value and predictive density: The predicted density of the natural gas price (response variable) in predicted observation 25 (2009) is shown.
The predicted observation is (13).
The predicted observation is obtained by applying the unscented transform again, this time using the predicted states, Xt+1|t, to estimate what we expect to observe from the hypothetical measurement of each state: γ = h ∈ ℝ m.
Within the framework of information filtering, the measurement residual e i,k at camera c i and time k is the disagreement between the predicted observation and the actual observation (see (11)).
Then, this predicted sigma points are weighted and recombined to produce the predicted observation vector widetilde{boldsymbol{rho }}_{ i,t)} = sumlimits_{l=0}^{2L}{{mathbf{W}}^{(s)}_{(l)} {widetilde{boldsymbol{gamma }}}^{(a)}_{(l)}} (24).
Equation (7) can be written as the predicted observation equation for a single landmark: h i ( x k | k − 1 ) = u i v i = c u + f k u x a i cam z a i cam c v + f k v y a i cam z a i cam (8).
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This model was preferred over models that might seem more mechanistically based because in internal validation, it attained a smaller PRESS statistic (the sum of squares between all observed and predicted observations) than other models.
If perfect prediction is detected, the perfectly predicting covariate and the perfectly predicted observations are dropped from the analysis.
A second alternative arises if the software detects perfect prediction before model fitting and drops the perfectly predicting covariate and the perfectly predicted observations from the analysis.
where z ̂ k is the measurement for all the M predicted observations.
Hence, the surprisal of the measurement residual e i,k gives the additional information associated with the received observations that is not available in the predicted observations through the predicted state.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com