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*From*: Paul Nord <Paul.Nord@valpo.edu>*Date*: Tue, 12 Oct 2021 13:34:54 -0500

I was hoping for some feedback on this analysis. Specifically, what did

you think of my conclusion:

"All of these models generate curves which are very close to the data.

While the errors seem very large, they are actually a better representation

of the true uncertainty in applying this model to this data. Many

least-squares fitting functions will give uncertainties which give too much

confidence in the model predictions."

Paul

On Fri, Oct 8, 2021 at 3:07 PM Paul Nord <Paul.Nord@valpo.edu> wrote:

I did a thing:

https://sites.google.com/valpo.edu/double-exponential-decay/

Surprisingly, the resulting uncertainties are bad while the fit looks

quite good. I've read that this sort of analysis gives a better estimation

of the true uncertainty than one often gets with least squares fitting.

Plotting a selection of 1000 models generated from this analysis shows that

they all lie very close to the mean. The assumptions of this model do not

constrain the model parameters given the particular data collected.

Said again: any of the terms in this model can be adjusted about 10%. You

can still get a similar and reasonable fit. You just need the right

tweaking of the other parameters.

Bad model assumptions here almost certainly include:

A) The background rate is constant

B) The stability of the detector

Paul

**Follow-Ups**:**Re: [Phys-L] Analysis of Half-Life measurement using PyStan***From:*John Denker <jsd@av8n.com>

**References**:**[Phys-L] re Bayesian Inference in Half-Life measurement***From:*Brian Whatcott <betwys1@sbcglobal.net>

**[Phys-L] Analysis of Half-Life measurement using PyStan***From:*Paul Nord <Paul.Nord@valpo.edu>

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