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In pymc, the way to do this is by defining a model using pm.Model(). You can define some distributions for your priors using pm.Uniform, pm.Normal, pm.Binomial, etc. To specify your likelihood, you can either specify it directly using pm.Potential (as I did above) if you have a closed form, otherwise you can specify a model based on your parameter using any of the distribution methods, providing the observed data using the observed argument. Finally, you can call pm.sample() to run the MCMC algorithm and get samples from the posterior distribution. You can then use arviz to analyze the results and get things like credible intervals, posterior means, etc.
I recognized that GCC wasn't originally designed as a compiler
The Double Asteroid Redirection Test (DART) mission was designed to demonstrate one possible way of deflecting such an object, targeting the non-threatening moonlet Dimorphos, which is about 560 feet wide. NASA quickly declared it a success after its initial analysis showed the planned collision shortened Dimorphos' orbit around Didymos, the larger of the two objects in the binary asteroid system. In a follow-up study published in 2024, a team at NASA's JPL reported that Dimorphos' orbital period had been trimmed by about 33 minutes, as its path was nudged roughly 120 feet closer to Didymos than before. The latest study now indicates that the whole binary system was affected, not just Dimorphos.