Book Comment 1

page 26, "0 corresponds to the baseline (no prior information) model",
what is the baseline?

--Baseline is ROC-A, with u=0 (no prior knowledge), described in step 1.

Is it always 0.68 without any variation? More information on the baseline(s) must
be given.

And why don't you plot the AUCs without baseline adjustment?

--Not understand Where 0.68 comes from. I can show mean value and std_error in
 one plot, rather than the 16-in-1 plot. If you want, I will send one to you.

page 26, "of of mu" <- "of mu"

page 27. what does "c" mean on the x-axis of Figure 1.4

c corresponds to the manually corrected arc, already in the text

Does the hidden state space dimensionality have any effect on the network
reconstruction accuracy? If it hasn't any influence,
then there seems to be no need for hidden states.
Why are 16 plots shown which all show exactly the same trends? The authors do not mention this finding in the text. They just mention that the "AUC performance can be seen to be linear with the number of prior arcs included" which is much less obvious from Figure 1.4.

It appears K has no influence for AUC. Should we show only one
plot with specific k and mu value, also variation will be plotted.  

page 28, Figure 1.5, why are there some bars missing in the histograms in Figure 1.5?

It means the replicates needed to achieve
the specific AUC is larger than 16, beyond the test setting.

Was there a threshold imposed on the maximal replicates (e.g. 16)?

Yes, we explored replicates with range 1,2,4,8,16

Wouldn't it be more intuitive to set those bars to this maximum
instead of leaving them out,

as higher bars indicate worse performances in Figure 1.5?

Bars would be more crowded 

There is no interpretation of the results. E.g. it is not mentioned
whether "mu" and "k" have an

effect on the inference results. 

mu and k have no obvious effect on results, we focus on replicates numbers.