Error floors in this Bayesian analysis

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The article discusses a Bayesian analysis of astrophysical body positions, utilizing Markov Chain Monte Carlo (MCMC) methods to address large uncertainties in these positions. A key focus is on estimating "error floors," which are systematic uncertainties that can affect measurement precision. The authors introduce a Hamiltonian Monte Carlo (HMC) sampler implemented in PyMC, which enhances sampling efficiency and allows for fitting error floor parameters directly within the model. This approach helps to mitigate systematic uncertainties that have previously hindered the accuracy of measurements in the context of astrophysical data analysis. Section 4 of the paper outlines the methodology and provides references for further exploration of the concepts discussed.
Artemisa
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It is not clear to me how the estimation of the floor errors is made in this article (https://arxiv.org/pdf/2001.04581.pdf).
In this article((https://arxiv.org/pdf/2001.04581.pdf)), the authors use a Bayesian analysis based on the positions of astrophysical bodies and their errors in the medians. This statistical analysis uses the markov chain monte carlo chains.

The uncertainties in the positions are large, so what they do is an analysis to estimate the floor errors.

My Doubt is
How do they get these error floors?
Could someone give me some reference or provide an example of how to do it?

Thank you very much for your attention
 
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Artemisa said:
How do they get these error floors?
Could someone give me some reference or provide an example of how to do it?
I have only skimmed the paper but section 4 at the top of page 9 seems to provide an outline with suitable references to follow up:
in this work we introduce instead a new algorithm utilizing the Hamiltonian Monte Carlo (HMC; Neal 2012) sampler implemented in PyMC36 (Salvatier et al. 2016). HMC methods take advantage of the posterior geometry to efficiently explore the "typical set" (i.e., the region containing the bulk of the probability mass) even in complex and high-dimensional spaces; see Betancourt (2017) for a concise overview of HMC. In addition to increased sampling effciency, the primary improvement provided by the new disk-fitting code is the ability to fit for the "error floor" parameters as part of the model, thereby removing a source of systematic uncertainty that has limited the precision of previous MCP measurements.
 
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