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The Metropolis–Hastings Markov Chain Monte Carlo algorithm was used to sample the parameters from the posterior distributions.
Although a combination of factors determined a valid sample, the parameters of the sampling templates were the primary constraint on the data collected from the image plane; other features of the simulation e.g., the speed and trajectory of objects, the length of time an object took to travel through the frustum affected the data only indirectly.
We sample the parameters excluded from the model from the pseudoprior (3) f (β \ γ ∣ β γ, γ, y ) ∝ f.
The sampling procedure is summarized by the following three steps (Dellaportas et al 2000): We sample the parameters included in the model by the posterior (2) f (β γ ∣ β \ γ, γ, y ) ∝ f (y ∣ β, γ ) f f, where y denotes the observed data.
The results of this study indicate that it is possible that the conflicting results could be related to variations in the way data were collected, the socioeconomic status of the sample, the parameters used to analyse nutritional status and caries diagnosis [ 29].
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If it is smooth enough, the Nyquist theorem limits the amount of information that is lost when we spatially sample the parameter vector d.
We use the massive parallel architecture of a Blue Gene supercomputer to sample the parameter space and characterize the rugged nature of the solution landscape.
To sample the parameter space with the goal of identifying the minima of the cost function, hundreds of thousands of runs were executed.
The SA algorithm searches for the global minimal GOF score and has been shown to efficiently sample the parameter space of simulation models with large number of parameters [33].
Therefore, it is very difficult or even impossible to sample the parameter space appropriately to generate confidence intervals for predictions.
To randomly sample the parameter sets from the hypercube defined by parameter ranges we used Latin Hypercube Sampling (LHS) algorithm [ 66].
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