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Various schemes have been proposed for this computation.
A broad range of decision-making processes involve gradual accumulation of evidence over time, but the neural circuits responsible for this computation are not yet established.
So overall, it takes longer for this computation to be carried out than it does in the case without any generalization.
Here we give the basic algorithm for this computation; we develop a fuller treatment of efficient techniques and approximations in Chapter 7.
Second, it presents a method for measuring bandwidth usage; the dialogue trees used for this computation are a new and compact representation of the probability distribution of every possible dialogue between two agents.
Napier discovered that the basis for this computation was a relationship between an arithmetical progression a sequence of numbers in which each number is obtained, following a geometric progression, from the one immediately preceding it by multiplying by a constant factor, which may be greater than unity (e.g., the sequence 2, 4, 8, 16... ) or less than unity (e.g., 8, 4, 2, 1, 1/2... )...
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An example of this computation for 3 treatments is shown in Additional file 2. We now present the proposed method for the estimation of the DPPs and the relative decrease in mortality that does not require the case fatality rate.
Selecting a suitable length of floats is helpful for stabilizing this computation.
For robustness, this computation was performed using the Cholesky decomposition.
For HLR, this computation is easy.
However, for posterior p-values, this computation needs to be repeated 50 times, once for each rate matrix.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com