Closed Jayshil closed 2 years ago
Hi,
Otherwise there is nothing necessarily wrong with large negative logz values.
Hi @segasai,
Thanks for your quick reply -- this was very helpful!
My dataset is consisting of a total of 32k points (and 14 free parameters) and not 90k points.
I don't know if you have a good fit. I.e. since you have an essentially a chi-square likelihood, you'd expect the peak logl(l) value to be of the order of -0.5*ndim (where ndim =90k). And the logz should be not too far from that. So you should check if your peak logl values make sense (i.e. correspond to reduced chisq of ~ 1)
According to this, my logl should be of the order of -0.5*32k=-16k? However, dres.logl[-1]
would give -976411898.6459092
which is nowhere near 16k (which is near to the computed logz though).
Thanks again, Jayshil
The logl value you get ( which corresponds to chi-square) is likely too low, but this really has nothing to do with dynesty, but instead with your data, model and goodness of fit of the best model.
Alright, thanks for your feedback. I will try fitting with some other models (and maybe priors) to improve the quality of the fit.
Hi,
Thanks for creating this wonderful package! It is much useful in my research. Recently, while using
dynesty
in one of my analyses, I ran into something peculiar.A bit of background: I am trying to fit a model (4 Gaussian + linear trend) to spectrum data with
dynesty
(version 1.2.2). I am using dynamic dynesty as follows:The output was,
62392it [53:13, 19.54it/s, batch: 4 | bound: 5 | nc: 34 | ncall: 2061630 | eff(%): 3.026 | loglstar: -976412117.210 < -inf < -976412116.450 | logz: -inf +/- 3.143 | stop: 0.999]
suggesting that the final calculated logz is -inf. And indeed when I printed
dres.logz[-1]
, it gave me-976412216.986027
. Since I am new to nested sampling, I am not sure if this is correct, or usual?!One thing should be noted that the posteriors were just fine -- it is just this logz calculation that bothers me. That makes me wonder whether I should believe posteriors or not.
By the way, a working example of the issue can be found in this jupyter notebook (along with the data used).
Thanks for your help, Jayshil