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View Code? Open in Web Editor NEWA custom Monte Carlo sampler for the (gravitational) two-body problem
License: MIT License
A custom Monte Carlo sampler for the (gravitational) two-body problem
License: MIT License
copy the relevant part of the peerless latex header
! Package pdftex.def Error: File `figures/exp2-corner-0.pdf' not found.
See the pdftex.def package documentation for explanation.
Type H <return> for immediate help.
...
l.574 ....9\textheight]{figures/exp2-corner-0.pdf}
! ==> Fatal error occurred, no output PDF file produced!
Transcript written on sampler.log.
make: *** [sampler.pdf] Error 1
they look like hell! Self-assigning.
OK, not exactly, but we need to add more words to the intro and discussion to explain why this is more desirable than using DNest. We want to explicitly state that you don't have to do any 2nd order tests of your samples to try to assess whether you are converged / have a true sampling from the posterior pdf. Starting with a dense enough (define) prior sampling, if The Joker returns many samples, you "just know" that you are done.
Switch to using velocity semi-amplitude, but still put prior on asini
This requires some text changes as well.
The instructions are (telegraphically) in the method section of the paper.
We should make a comment that we actually have a prior belief about v0
even though we don't impose it -- if disk star, v0 ~ N(0,30) km/s
; halo star, v0 ~ N(0,150) km/s
, etc.
Add words to the Discussion talking about incorporating more linear parameters into the model.
To keep track of units, transform to / from emcee
sampling parameters (e.g., e,\omega <=> \sqrt{e},\cos\omega, \sqrt{e},\sin\omega)
... in my bw print out of figs 1 4 8
Right now run-sampler.py
generates new batches of prior samples each time it is run because it only takes minutes (low incentive to optimize). But for the experiments, I should just generate a massive file of samples and use chunks of the same samples for all experiments.
Technically, the posterior will always be multimodal. All we are asking for is effective unimodality. This requires an audit of the text
When plotting Troup's parameters over what we find in Experiments 2,3,4, the color for Troup should be different from plotting the True parameters for fake data in Experiments 1,5
A slightly more general version is:
"The Joker: A custom Monte Carlo sampler for periodic radial velocity data"
but that is boring as hell and doesn't catch the eye as well as explicitly mentioning binary stars and exoplanets. I don't have a strong opinion, but want to raise the point for discussion.
And Bedell
emcee
...or in fact before every \section{}. The idea behind this issue is to clear all the floats before we discuss.
This project is also relevant for supermassive black hole binary searches and population inference:
https://ui.adsabs.harvard.edu/#abs/2016arXiv160401020C/abstract
https://ui.adsabs.harvard.edu/#abs/2015MNRAS.453.1562G/abstract
I think maybe there are some \ref{}
issues; there are experiments and there are section numbers, but these aren't the same.
Hash on something like (re-run number, worker rank / chunk index)
Can we do a good case that is effectively unimodal as well?
This is a mpl bug, but let's fix it
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There is a list at the top of the LaTeX file. This issue can be closed when that list is empty.
Just sayin'
(Hogg thinks: no)
The latex labels for the parameter names should automatically add the units, not hard-set like they are now.
latex_labels = [r'$\ln (P/{\rm day})$', '$e$', r'$\omega$ [deg]', r'$\phi_0$ [deg]',
's [m s$^{-1}]$', r'$K$ [m s$^{-1}$]', '$v_0$ [km s$^{-1}$]']
We could make data with ZERO signal, and then:
In the latter case, we should find some kind of posterior pdf... What?
Like the software tag, there is a facilities tag, which should say sdss-iii or apogee
I want a parameter s-squared
, such that the noise variances become
sigma^2 + s-squared
This has to be sampled along with the other non-linear parameters, so we have to go to 5-d sampling (and probably 2 ** 29
samples).
Put a prior on s-squared that is Gaussian in the LOG of s-squared.
@adrn, can you try doing this and see if everything still works? I only ask because all our customers want this.
In the discussion we should raise the issue that if you have (a) crappy, sparse data, or (b) a lot of good quality data, and all you want to do is learn about the system, you don't need to start with so many prior samples (many will pass the rejection step). It's really the intermediate regime where it is important.
But, if you want to do hierarchical modeling, or if you want to assess whether or not your posterior is multi-modal, you need to sample the hell out of your priors.
We should cite his DNEST but ALSO cite his paper (he has one, I think) on exoplanet RV inference.
No good deed goes unpunished.
They always look uniform, like the prior...
I MUCH prefer M
because in general, for exoplanets, our dumb noise model won't be good enough.
Add some conclusions to each experiment because the discussion section is too late!
Experiment 1 - we do get samples at truth, yes more weight at short periods, but we're actually sampling.
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