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In this regard, we follow Pistorius and Mijatovic (2011) who show how to approximate a Lé vy process by a continuous time finite state Markov chain and use the approximation to price barrier options.
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For testing of the covariates, the different models were compared using the approximation to the χ distribution of the objective function value of the reduced model minus that of the full model.
In this paper, we use the approximation error modelling theory to explain the connection between the IST and PIM approaches.
If we consider the network within E. coli, one can use the approximation that 1 molecule corresponds to a concentration of 1 nM.
Fowlkes et al. propose to use the Nyström approximation to avoid calculating the whole similarity matrix.
We can use the linear approximation to compute any such inverse function to the accuracy that our machine will allow, as we shall see next.
However, in most cases, the linear trend approximation is sufficient; in other cases, we use the linear approximation to facilitate comparison with trends in other parameters.
When the refractive index changes very slowly compared to the wave-length we may use the eikonal approximation to the wave equation.
Due to the mathematically intractable expression of the Rician distribution, we use the Gaussian approximation to derive the error probability of coded FHSS over Rician fading channel.
We use the QLE approximation to track allele frequencies at each locus separately.
We use this approximation to delimit the region of parameter space (λ̅, w+) where the network shows tristability among the spontaneous and the two decision states.
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