gaussian.py discrepancy between theory and simulated result

[qacwnfq]

Hey,

I was just running the gaussian.py example locally and I found

log(Z) = -14.94080654452895 [exact log(Z) = -11.512928331486771]

Is this discrepancy between the sampled and theoretical result expected?

I've appended the .txt files that are created during sampling to this issue.

Thanks!

levels.txt sample.txt sample_info.txt sample_log_X.txt sampler_state.txt stats.txt weights.txt

[qacwnfq]

One thing I noticed in the prior is, that it is is not sampling symmetrically around 0. Right now it is like this:

def from_prior(self):
        return np.random.uniform(-0.5*self.width, self.width,
                                    size=(self.ndim,))

I think it is meant to be like this:

 def from_prior(self):
        return np.random.uniform(-0.5*self.width, 0.5*self.width,
                                    size=(self.ndim,))

But that doesn't fix the issue.

[qacwnfq]

So if i check the posteriors for the C++ version of this example (PR #38) I get:

which is quite different from

There are some clear visible differences. I will try to investigate if they arise within the gaussian.py file.

[eggplantbren]

Thanks for looking into it. I'm very occupied at the momentSent from my Galaxy -------- Original message --------From: Johann Fredrik Jadebeck @.> Date: 31/08/21 00:15 (GMT+12:00) To: eggplantbren/DNest4 @.> Cc: Subscribed @.***> Subject: Re: [eggplantbren/DNest4] gaussian.py discrepancy between theory and simulated result (#37) So if i check the posteriors for the C++ version of this example (PR #38) I get:

which is quite different from

There are some clear visible differences. I will try to investigate if they arise within the gaussian.py file.

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[qacwnfq]

So in the C++-Version it seems to get close enough to the analytical solution. For now I will continue with the C++-Version :)