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Why do we need a trace plot for MCMC?
You create the parameter trace plots to make sure that your a priori distribution is well calibrated which is indicated by your parameters having sufficient state changes as the MCMC algorithm runs. An extreme example is that you set your a priori distribution variance at 0.
Can a MCMC draw be a 3 D array?
A 3-D array, matrix, list of matrices, or data frame of MCMC draws. The MCMC-overview page provides details on how to specify each these allowed inputs. It is also possible to use an object with an as.array () method that returns the same kind of 3-D array described on the MCMC-overview page. An optional character vector of parameter names.
How to highlight a chain in MCMC trace?
For mcmc_trace_highlight (), passed to ggplot2::geom_point () to control the transparency of the points for the chains not highlighted. For mcmc_trace_highlight (), an integer specifying one of the chains that will be more visible than the others in the plot.
What does a trace plot of parameters indicate?
What does a trace plot of parameters indicate? You create the parameter trace plots to make sure that your a priori distribution is well calibrated which is indicated by your parameters having sufficient state changes as the MCMC algorithm runs. An extreme example is that you set your a priori distribution variance at 0.
Which is the vignette plotting package for MCMC?
Plots of parameter estimates from MCMC draws are covered in the separate vignette Plotting MCMC draws, and graphical posterior predictive model checking is covered in the Graphical posterior predictive checks vignette. Note that most of these plots can also be browsed interactively using the shinystan package.
Why is chain 3 slow in MCMC diagnostics?
In the third plot (Chain 3), there is a lot of serial correlation between successive draws. The chain is very slow in exploring the sample space. The sample space has been explored only few times. In other words, there seems to be few independent observations in our sample.
What happens when a chain converges in MCMC?
When the chain converges slowly, a large portion of our MCMC sample might be made up of observations drawn from distributions that are significantly different from the target distribution. If we are able to spot this kind of problem, we can try to fix it by: discarding a large chunk of initial observations (the so-called burn-in sample);