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- The trace plot has been proposed (originally in the 1980s already) to illustrate the interplay of effect estimates and (estimated) heterogeneity. It is useful in the context of (“simple”) meta-analyses or meta-regressions, and may be derived in the context of Bayesian as well as frequentist approaches.
onlinelibrary.wiley.com/doi/10.1002/jrsm.1693
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What is a trace plot?
What is a trace plot for a Markov chain?
What is a trace plot in MCMC?
What motivates a trace plot?
What does a trace plot of parameters indicate?
What is the difference between a rank histogram and a trace plot?
A trace plot is a line chart that has: time () on the x-axis; the values taken by one of the coordinates of the draws (e.g., if the -th coordinate is plotted) on the y-axis. The next figure contains three trace plots that illustrate typical situations we may encounter.
plot_trace is one of the most common plots to assess the convergence of MCMC runs, therefore, it is also one of the most used ArviZ functions. plot_trace has a lot of parameters that allow creating highly customizable plots, but they may not be straightforward to use.
Whereas traditional trace plots visualize how the chains mix over the course of sampling, rank histograms visualize how the values from the chains mix together in terms of ranking. An ideal plot would show the rankings mixing or overlapping in a uniform distribution. See Vehtari et al. (2019) for details. mcmc_rank_overlay()
A trace plot is a graphical representation used to visualize the sequence of samples generated by a Markov Chain Monte Carlo (MCMC) method. It helps assess the convergence and mixing of the MCMC algorithm by displaying the sampled values for each parameter over iterations, allowing for visual inspection of whether the samples are moving freely ...
Dec 15, 2023 · The trace plot allows visualization of the sensitivity to τ along with a plot that shows which values of τ are plausible and which are implausible. A comparable frequentist or empirical Bayes version provides similar results.
May 23, 2018 · An MCMC algorithm aims at a given posterior distribution, irrelevant of the choice of the prior, and under proper conditions creates a Markov chain that converges to this stationary distribution. Looking at trace plots is only useful in assessing the convergence or lack thereof of the Markov chain. $\endgroup$ –
A trace plot provides a visualization of a Markov chain's longitudinal behavior. Specifically, a trace plot for the \(m\) chain plots the observed chain value (y-axis) against the corresponding iteration number (x-axis).
