Marginalized constraints: These values are obtained by marginalizing the posterior probability distribution, representing the uncertainty of the parameters. The calculation considers the entire posterior distribution of the parameter space, making it more stable and accurate.
Best fit: This is the point estimate derived from maximum likelihood estimation, which may exhibit some instability due to sampling at a single point. When sampling is insufficient or the likelihood distribution shape at that point is not ideal, anomalous cases of lower1 and upper1 may occur.
Best fit: This is the point estimate derived from maximum likelihood estimation, which may exhibit some instability due to sampling at a single point. When sampling is insufficient or the likelihood distribution shape at that point is not ideal, anomalous cases of lower1 and upper1 may occur.
Statistics: Posted by Junxian Li — September 24 2024