inter

Part of Frequency Table for Shift Subset

Download: freq_EColi_post_as_prior.pdf

Notes:
1. Shift Subset is defined from Page 16 of Manchester.pdf

2. In muD (prior mean matrix of D), only 12 entries'signs are adjusted, based on the info in memo1. Only these 12 entries are multiplied with mu value (e.g. *0.5), when mu varies. Other entries inherit their sign and value from previous experiment.

3. 10 posterior MEAN matrices for A,B,C, D from the previous experiment (vsn_normalization), with some entries adjusted for the new priors. However, vbssm is unable to run when posterior COVARIANCE is incorporated, since 'trigamma' function will report severe problem to stop computation.
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Memo1:Priors 
hns-> glpC, glpQ +ve (these appeared in the model network and were confirmed by the experiment)
hns-> cyo D,E,B,A no connection (not confirmed by experiment)
hns -> sdhB -ve (confirmed)
hns-> arcA no connection
hns-> appY -ve (connected confirmed but sign different)
hns-> cad A,B -ve (opposite sign)
hns -> hdeB -ve (opposite sign)
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Memo2. Posterior
Expt A, instead of starting with 10 random seeds, you need to start from the 10 posterior matrices for A,B,C, D from the previous experiment (vsn_normalization), with the means and variances adjusted for the new priors, i.e. the posteriors from the previous experiment become the priors for the new experiment.
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Note:
Q: How to present 'no connection' in prior matrix?
A: This is be a prior constrained around zero - i.e mean zero but with very tight distribution (low variance)

Set Prior Covariance Matrix for A,B,C,D

vbnet examples: mu = 0 (no prior), mu = 0.1 (with prior)

Download: ARD derivation notes

Data: Zak's data
reps = 4;
kk = 6;
arc = 9; (arc 9 is added as prior arc)
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Hi Juan

I guess so, how did you specify priors for the ARD prior experiments?
You need to set the mean and variance I guess, which would just be the diagonal of the full covariance matrix.
-- In ARD expr,
where delta = 1*ones(pinp,1) % by default initialization in Matt's code.

So, I only need to set the mean, let variance be default -Juan

I didn't realize the code output the full covaraiances - maybe we can look at the posterior covariances to understand correlation between the parameters as I suggested earlier - can you put some samples from the ARD prior experiments (Zak's data) on the web site?
- See top

Let's try to talk at 8am Friday if that works for you. If not then Thursday 8am would also work for me

- OK, Friday 8am.

David
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I happen to realize that, vbssm specifies the Prior Covariance matrix of D as diagonal, not full matrix.
(see the attached derivation Page 2, Equation 7)

But, I have checked that the Posterior Covariance matrix of D obtained from the previous experiment (vsn_normalization) is actually a full matrix. How should we treat it? Is it OK to let the non-diagonal entries be zero, in order to fit the vbssm model?

-Juan

F vs kk for "E_Coli_ no_inter_sheet"

Hyper-opt is on_______ Hyper-opt is off

Download: PDF
Data: E.coli.values.xls, no-inter sheet (normalized by Juan afterwards)
Hyper-optimization = on ( same as vsn-normalization.xls)
its = 2000.

Compared with Figure 5 of Zak's data (below), F_kk figure above seems make sense.

Hyper-opt is on_______ Hyper-opt is off