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To circumvent the intractable integration in the Bayesian formula, importance sampling technique is used to obtain samples which follow the posterior distribution.
Applying this even for a single velocity step yields an intractable integration, but a suitable approximation is constructed that allows it to be evaluated in analytical form.
According to eq. (30), the predicted step for f n|L(n' − 2) + ll (α n ) involves the sum of two terms: birth and survival parts, which should be approximated numerically due to the intractable integration on the continuous distribution.
For arbitrary non-linear observation models, computing the posterior distribution poses an intractable integration problem [70].
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However, seeking a closed-form solution to (12) and (13) is intractable due to the integration over θ.
The usual numerical integration becomes intractable as the number of speakers increases because the dimension of the integrated variable space increases and the computational cost increases exponentially.
Nevertheless, the E-step usually fails quickly when the number of censored data and/or the dimension of random effects increases since numerical integration becomes intractable in high dimensional situations.
In high dimensional spaces where Polynomial Chaos or other direct integration techniques become intractable, sampling methods may be the only way to compute integral functions in probabilistic analysis.
This latter quantity is intractable since it requires an integration over the parameters (θ ∗, d∗, b∗, s∗).
Since the integration here is analytically intractable, the Gibbs sampler, a Monte Carlo approach to calculating marginal densities, is employed.
Because the explosion in the number of variables in complex networks makes parameterization intractable at the cellular scale [ 15], integration of the TGF-β signaling networks clearly requires other methods.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com