Skip to content

PyMC sampling results do not show concentrated distribution #140

@SakakinoKonomi

Description

@SakakinoKonomi

Hi, when I applied the code from The Joker tutorial section 'Continue generating samples with standard MCMC' to a source with good periodicity, the rejection sampling in the first step of The Joker was able to produce a high-quality orbital solution.

Orbital parameters obtained from The Joker sampling: period (P) = 2.77381769 days, eccentricity (e) = 0.02842552, semi-amplitude (K) = 78.02635686 km/s, systemic velocity (v₀) = 17.84151884 km/s

Phase-folded diagram plotted using the sampling results.
Image

However, the subsequent MCMC sampling results exhibited a widely-separated multi-modal distribution, making it impossible to plot a clean, Gaussian-like peak. I used 8 chains with 1000 sampling points per chain as the parameters for MCMC sampling.
Image

Here is the posterior probability histogram.
Image

However, when I generated the corner figure using only the chain with the highest posterior probability, it produced a relatively good result, even though this doesn't represent the true orbital parameters.
Image

Here is the code I used.
https://github.com/SakakinoKonomi/project/blob/main/RunJoker.ipynb

Metadata

Metadata

Assignees

No one assigned

    Labels

    No labels
    No labels

    Projects

    No projects

    Milestone

    No milestone

    Relationships

    None yet

    Development

    No branches or pull requests

    Issue actions