-
Notifications
You must be signed in to change notification settings - Fork 117
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Size chain. #120
Comments
MontePython detects the "burn in" at the beginning of each chain (roughly speaking, the phase in which the likelihood is regularly decreasing towards the best fit, instead of fluctuating in a small range above it). Technically the burn in is defined by MP as "all the points until the step at which the effective chi2 becomes for the first time smaller than the minimum chi2 in the chain plus six". If you have 100 points, they could all be identified as burn in, removed, and nothing will remain apart from a couple of points. Then the code returns the error message that you saw. So you must overcome burn in. In addition, note that all the result from MCMC parameter extraction that you see in the literature are based on runs with about 10'000 to 1'000'000 points after burn in... 100 is nothing! |
I forgot to say: if you are sending an issue to this website it means that you are not aware that the official montepython_public GitHub has moved to https://github.com/brinckmann/montepython_public |
Thank you so much! I'm just not sure if my laptop will survive 10,000+
points, I will try though. Thanks again.
El lun., 22 de octubre de 2018 0:33, lesgourg <[email protected]>
escribió:
… MontePython detects the "burn in" at the beginning of each chain (roughly
speaking, the phase in which the likelihood is regularly decreasing towards
the best fit, instead of fluctuating in a small range above it).
Technically the burn in is defined by MP as "all the points until the step
at which the effective chi2 becomes for the first time smaller than the
minimum chi2 in the chain plus six". If you have 100 points, they could all
be identified as burn in, removed, and nothing will remain apart from a
couple of points. Then the code returns the error message that you saw. So
you must overcome burn in. In addition, note that all the result from MCMC
parameter extraction that you see in the literature are based on runs with
about 10'000 to 1'000'000 points after burn in... 100 is nothing!
—
You are receiving this because you authored the thread.
Reply to this email directly, view it on GitHub
<#120 (comment)>,
or mute the thread
<https://github.com/notifications/unsubscribe-auth/Alznh08X4U6LPqM-sFLdvOdfzcAWJn7Eks5unXTmgaJpZM4Xy1Tf>
.
|
MontePython gives me an error that no decently sized chain is found, I honestly don't have a clue, maybe it means I have to run it with more points? It used to work -N 100 though.
I appreciate all comments, thanks in advanced.
The text was updated successfully, but these errors were encountered: