ChainsMakie

ChainsMakie implements several plotting methods and recipes for visualizing Markov chain Monte Carlo (MCMC) chains in Makie. Each plot type highlights a different aspect of the MCMC sampling, helping to diagnose potential problems.

The man entry point to the package is the plot method for a Chains object.

Quick start

Here's how to create the most common visual summary of MCMC chains:

using ChainsMakie, CairoMakie
import MCMCChains: Chains
chains = Chains(randn(300, 3, 4), [:A, :B, :C])
plot(chains)

If the model parameters are in a similar range, you might prefer linking the x-axes to give it an even cleaner look:

using ChainsMakie, CairoMakie
import MCMCChains: Chains
chains = Chains(randn(300, 3, 4), [:A, :B, :C])
plot(chains; link_x = true)

For all plotting functions exported by ChainsMakie, it is possible to plot a subset of the parameters by passing the parameter names to be plotted as the second argument to the plotting function:

using ChainsMakie, CairoMakie
import MCMCChains: Chains
chains = Chains(randn(300, 3, 4), [:A, :B, :C])
plot(chains, [:A, :B])

using ChainsMakie, CairoMakie
import MCMCChains: Chains
chains = Chains(randn(300, 3, 4), [:A, :B, :C])
trankplot(chains, [:A, :B])