Import model after setting random seed.

Change the order of imports. As importing fa may invoke random number
generation let's set the random generator seed before importing fa.
Minor: delete obsolete comment
Increase symmetry: Instead of C-Myc-E as inits use random values
Minor: Step in re-ordering priors
Minor: Step in re-ordering code lines of priors
Minor: Step in re-ordeing code lines of priors: Swap title order

Old order: variable, parameter type
New order: parameter type, variable
Minor: Step in re-ordering code lines of priors: delete duplicate section titles
Vectorise b, c, s, tau in the priors block

Step in vectorising the parameters:
Substitute (C_Myc_bE, C_Myc_bF, CD31_bE, CD31_bF) variables with b vector.
Substitute (C_Myc_cE, C_Myc_cF, CD31_cE, CD31_cF) variables with c vector.
Substitute (C_Myc_sE, C_Myc_sF, CD31_sE, CD31_sF) variables with s vector.
Substitute (C_Myc_tauE, C_Myc_tauF, CD31_tauE, CD31_tauF) deterministics with tau vector.
Minor: simplify code bocks and comments
Step in vectorizing: use b, c, tau vectors in the multiple linear resid calculations

Minor: Introduce NCases instead of the multiple uses of constant 20.
Minor: Introduce Zero_times_NCases instead of the multiple uses of
numpy.zeros((20,), dtype=float).
Step in vectorizing relationships between the latent x and the observed
values: use b, c, tau in the multiple linear resid calculations and the
normal probabilities of residuals.
Minor: improve documenting comments
Join IHP data vectors into a 2D array
Matrix: replace output vectors with matrix in linear model calculation

Transpose x, c.
Convert b, c, x to matrix.
Use 2D zero matrix instead of Zero_times_NCases.
Replace multiple linear resid and normal calculations for each Ihp
outcome with a single matrix calculation.
Fix array vs matrix type and matrix shapes

- Convert b, c back from matrix to array (otherwise pymc failed).
- Calculate residuals in 2 steps to help debugging.
- Fix matrix shapes in the resids_normal calculation.
Remove irrelevant variables before codamenu()
Improve code removing burn-in

- comment
- display coda matrix dimensions before and after burn-in removal
- convert object type back to mcmc after manipulation
Indent and document code block # Load in raw data and perform the standard transformation
Indent and doc code block # multiple linear regression explaining latent x result with Ihp variables
Adapt to altered variable names of the sigma parameters
Run results

- Posterior s ~ unif, same as prior.
- Despite the above latent x is close to C-Myc-E.
- x -- C-Myc-E correlation is .99 only (not 1.00), but x -- CD31-F
correlation is 0.25 (vs C-Myc-E -- CD31-F -0.16).
- Somewhat lower thin may be enough