Book comment --2

(Sec 1.4):

Does the Bayesian analysis provide confidences or error bars?

-        Yes, I can show one plot in figure 4 with error bars.

It is more common for the variable of integration (d\theta) to be at the end of

the integral rather than first.

- Would be a lot of work, suggest not take this modification.

(Eqn 1.33):

If \gamma_j is the precision, should the inverse {\gamma_j}^{-1}, the variance,

appear in the Normal distribution in the equation.

-- Give me more time, I will respond soon.

More could be said about the Biology in the final paragraph in Section 1.4.3.

top of page 18:

spaces missing between A,B,C

(Eqn 1.47):

suggest move dX's after the function?

I'm confused by the text on page 17. Is C_{(s)} a column or ROW of C.

If it's a column, then doesn't C have K columns? and already denoted c_j?

C_(s)^T is the the s-th row of matrix and follows a Gaussian distribution.
Therefore, s = 1,…,p

Can \rho_s and \gamma be defined/introduced here, prior to their use in

diag(\rho_s,\gamma).

This section uses a lowercase k, whereas earlier section 1.2 began with

uppercase K, it would be good to be consistent.

(Sec 1.4.2.3, pg 21):

First line, mis-positioned bar should be above theta.

Eqn 1.75: small k

(Sec 1.4.2.4, first bullet):

Infer -> infer 

(Sec 1.4.2.5,  third bullet, equation):

What's Z'  (should there be a prime on the Z? (why Z'))

Change Z’ to Z (the symbol  in picture as well) 

Sec 1.5.1:

In section 1.5, you introduce a synthetic network with 54 players (illustrated in Fig 1.3), and generate expression levels for 10 mRNAs. When it comes to the evaluation and the construction of ROC curves, what is the ground truth to which you are comparing? 

12 edges:

A -> A, A -> B, A -> C,       , C -> D, C -> G, C -> K

D -> C, D -> E,       , F -> B, F -> D, G -> H, K -> J

Knowledge about the size of

the model that you are trying to recover (whilst acknowledging that there's a complex process actually generating the data) is useful, particularly its

topology. Possible structures on 10 nodes have between 0 and 100 edges.

Stating the network, gives an indication of how much information adding 

1, 2, or 3 prior interactions has.

Is it the network with the following 14 edges?

A -> A, A -> B, A -> C, B -> A, C -> D, C -> G, C -> K

D -> C, D -> E, E -> F, F -> B, F -> D, G -> H, K -> J

-No edges E -> F or B ->A

An image of this network would be a good figure to add.

I can draw it if you think necessary

How many edges are inferred by your methods? -i.e. if you had to choose a "best" point on the ROC curve? 

We tried to infer 12 edges, and performance is measured by AUC.

There are a number of related questions of interest, for

example, how many edges would you need to infer to get all the edges present in

the ground truth (mRNA 10 node) model correctly inferred?

Don’t test yet.