Standard errors of parameters fitted with sbiofit using Bayes are too high
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Hi,
I am currently working with SimBiology and have implemented a compartmental model to follow the Lutatera therapy. I am using the 2021 version of Matlab. I have modified sbiofit to implement the object function in order to perform a fit with Bayes values. Comparing it with another software that already uses Bayes, I could see that the fitted parameters have values that differ little. The problem is that with matlab, the standard errors that sbiofit calculates are too large. At first I thought it was just because of the degrees of freedom, but unfortunately, even when correcting them, I still get too high errors on the fitted parameters. Upon redoing the code, I noticed that the Jacobian does not take into account my Bayes values, nor the standard Bayes error. numjac is the function that calculates the Jacobian.
My question is whether it is possible, and how, to incorporate Bayes standard errors into the standard errors of my parameters. In theory, from the fit, the errors of the fitted parameters should be no larger than the Bayes errors. Has anyone ever solved this problem in other functions used for the fit? Or are there functions that already use Bayes that can help me solve my problem?
Regards
2 comentarios
Florian Augustin
el 15 de Nov. de 2022
Hi Valentina,
SimBiology's function sbiofit currently (R2022b) does not support Bayesian approaches for parameter estimation. If you would like to implement Bayesian parameter estimation, you could take a look at mhsampler or hmcSampler in the Statistics and Machine Learning Toolbox. I am planning on making a FileExchange submission for Bayesian parameter estimation using mhsampler with SimBiology models soon. I can post a link here if you are interested.
To your actual question. Can you post more details about the algorithm you implemented? Any reference, code that we can run to reproduce the issue would help the community to answer your question.
Best,
Florian
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