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Dec 13, 2007, 7:24:01 AM12/13/07

to SuperBayeS Users

So I used SuperBayes to do an ordinary, standard grid scan for

comparative purposes and was wondering what would be the best way in

general to plot it?

The standard output from SB will be a posterior plot and a likelihood

plot, both of which assign weights/probabilities which in the case of

a grid scan I'm guessing are not correct/relevant. My solution was to

take the likelihood plot and assign all points with non-zero

probability with an equal importance (i.e all the same color), is this

an acceptable approach?

I much appreciate any help given!

comparative purposes and was wondering what would be the best way in

general to plot it?

The standard output from SB will be a posterior plot and a likelihood

plot, both of which assign weights/probabilities which in the case of

a grid scan I'm guessing are not correct/relevant. My solution was to

take the likelihood plot and assign all points with non-zero

probability with an equal importance (i.e all the same color), is this

an acceptable approach?

I much appreciate any help given!

Dec 13, 2007, 7:55:26 AM12/13/07

to SuperBayeS Users

> The standard output from SB will be a posterior plot and a likelihood

> plot, both of which assign weights/probabilities which in the case of

> a grid scan I'm guessing are not correct/relevant. My solution was to

> take the likelihood plot and assign all points with non-zero

> probability with an equal importance (i.e all the same color), is this

> an acceptable approach?

with weight 1 (as in this case there is no probabilistic significance

to the points). The only relevant quantitity is the log like, as you

say.

I'm not sure what you mean precisely by "standard output" - is this

after you processed the chains using getplots? For chains produced

with grid scan I would advice not to use getplots at all, as the

posterior plot would be meaningless. The average like might me more of

a useful quantity to look at, but again would have no statistical

significance. Better to either plot the points in relevant 2D planes

by hand from the chains directly, or simply select the highest

likelihood along the hidden dimensions - this is somewhat analogous to

profile likelihoods. I'm not sure your procedue can be easily

interpreted statistically. It all depends what are you trying to

deduce from the scan...

Hope this helps, let me know if you have further questions.

Roberto

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