Questions about the model fitting example about george HOT 9 CLOSED

The answer to both of your questions is the same: sigma == yerr!

Also: The var(y) is the initial guess for the amplitude of the Matern kernel.

from george.

Oh I see. So by default the white noise kernel is added, where sigma is yerr.

One other related question. Once you define the log likelihood, as you did in the tutorial, why aren’t the kernel hyper parameters, e.g. length scale and amplitude, included in the mcmc sampler? In other words is it possible (or even necessary) to get a posterior on both the model parameters and the noise parameters?

from george.

It's not quite a jitter term: it's heteroscedastic (the values can be different for each data point) and it won't be included in predictions.

The kernel hyperparameters certainly are included in the MCMC! There might be scenarios where that's not needed, but you'll generally want to marginalize.

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Is there a way to extract the hyperparameter samples like the sigma, amplitude and length scale?

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I mean, that's what's happening in the tutorial: https://george.readthedocs.io/en/latest/tutorials/model/#the-final-fit

from george.

Look at gp.get_parameter_names() to figure out the names.

from george.

you're absolutely right! For some reason I thought those five parameters were just from the f(t) model. The last two parameters are the matern kernel params:

('mean:amp', 'mean:location', 'mean:log_sigma2', 'kernel:k1:log_constant', 'kernel:k2:metric:log_M_0_0')
Is there a way to add a white noise kernel parameter? I would like to add \sigma_i to the gp parameters shown above in addition to the two matern32 hyperparameters. It seems like you are hard coding it as yerr?

from george.

Yes this is explained in several places throughout the documentation (e.g. https://george.readthedocs.io/en/latest/tutorials/hyper/). Perhaps you should spend some more time with the docs and try to work out these answers yourself because there's only so much support I can provide!

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Thanks for all your help. I really appreciate it.

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