In an attempt to fit an ellipse to the orbital motion of Jupiter’s Galilean moons over a month, a researcher employed Markov Chain Monte Carlo (MCMC) methods. The ellipse equation involves 5 parameters: x0 and y0 for the center, a and b for the semi-major and semi-minor axes, and theta for rotation. The initial fit used the least squares method to set the prior function’s parameters. The MCMC was run using emcee, aiming to define parameter errors as the 15th and 85th percentiles, assuming a Gaussian distribution around the best fit. However, the corner plot revealed that the walkers were distributing at the prior function’s boundaries, not in a Gaussian manner. Expanding the prior boundaries helped some parameters but caused others to skew. The researcher noted that fitting an ellipse can yield two solutions due to rotational symmetry, initially causing bimodal parameter distribution. Attempts to reparametrize the model, such as using eccentricity ‘e’ instead of ‘b’, and adjusting prior boundaries for parameters ‘a’ and ‘theta’, only partially resolved the issue.
ʚ파피ɞ
@0312asterum
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Mar 20 hamin-ah, this mc thing is pretty hard?
it’s hard? why?
it’s not easy. you can’t do anything once you start spacing out
you shouldn’t be spacing out to begin with
a-ah.. I see
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