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It was possible to make this distinction by parameterization of the model using distribution volumes (the pharmacological term) and clearances instead of rate constants which depend on distribution volumes and clearances.
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Forest dynamics were mostly modelled using distribution models, gap models and distance-dependent tree models.
We implemented these distributed factors in a unified semi-Markov temporal-difference-based reinforcement learning model using a distribution of µAgents, the set of which provide a distributed discounting factor and a distributed representation of the believed state.
The model is solved by using distribution free approach with known mean and standard deviation.
To assess the robustness of our result, we performed a probabilistic sensitivity analysis (PSA) on the model using appropriate distributions for each variable.
Again, the informed variance model using frequentist approximate distributions had the highest posterior distribution precision.
The logistic regression model using the current distribution of mugwort and Japanese knotweed at the US county spatial scale performed fairly well, describing 24 30% of the variation.
A generalized linear model using a gamma distribution was used to control for these differences.
They were then entered in the lower level of the hierarchical model using the normal distribution.
Model glm.nb1* (eq.1) had an AIC of 554, while the model using negative binomial distribution including gender had an AIC of 485 (model glm.nb3, eq.4).
Among the five heterogeneous variances MTC model, the consistency variance model and the informed variances model using frequentist approximate distributions produced estimates closest to the frequentist ones.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com