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We isolate three main contributions as follows: First, a stochastic fit for the main field signal is obtained separately for each observatory using a Gaussian process regression that includes our prior knowledge of the core field temporal cross-covariances.
First, a stochastic fit for the main field signal is obtained separately for each observatory using a Gaussian process regression that includes our prior knowledge of the core field temporal cross-covariances.
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Likewise in the case of a stochastic trend, fitting of a VAR model upon differencing is inadequate if the series are cointegrated [ 25].
From preliminary calculations with first-order and fractional-order stochastic fits, we find that if a Wiener (Brownian motion) term is used for the stochastic term, then the KDE approximation method gives variances (sigma_{G}) that are very small and fits that are close to the deterministic model fits.
(2007) used a stochastic approach to fit their tumour model on clinical survival data.
In order to test these hypotheses, we need to select a stochastic model that fits the dynamics of the rate of change of glucose concentration with respect to time.
The distribution of error, as illustrated in Figure 1, does not fit a stochastic model, for any error type.
We accounted for this by using a data-augmented Markov chain Monte Carlo algorithm to fit a stochastic transmission model to the observed data (19– 219.
However, we do not believe that there is enough data to make it feasible to fit a stochastic model that would take into account intrinsic noise.
The fourth approach used surveillance data from arbitrarily timed swabs to fit a stochastic transmission model using a likelihood approach, making use of observed stays of individual patients (27).
MLE is a practical method for fitting a stochastic model to a data set.
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