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Yes, tens of minutes for the model to run is a lot. If you are using a gradient based minimization algorithm such as BFGS to calculate the parameters, you might consider using the adjoint method for computing the gradient very efficiently.


50 is a lot of parameters. You could try doing a basic first order sensitivity analysis to determine whether you can drop any of these. Using Bayesian Optimization to minimize a cost function is one way of dealing with the problem you've encountered. But remember that your standard L2 norm might have counterintuitive behaviours in high dimensions (see On the ...


The previous answer pretty sums up my understanding on this problem. I just want to add 2 solid references on this regard (Both are from an astrophysics context). The paper by Hogg et al provides a pretty hands-on approach while the the survey of Sharma is more of a survey of MCMC analysis usage in astrophysics. I am not from the astrophysics community, but ...


As I understand, your ultimate goal is to solve an inverse problem (i.e., infer some parameters from given data / observations). To this end, you want to apply Bayesian Inference, which relates the posterior (i.e., the probability distribution of the unknown parameters) to the likelihood (i.e., the probability model of observing some values given the ...

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