- Trace of the chains
- Statistics (mean and var)
- Marginals (KDE/Histograms)
- Autocorrelation plots
- Show multiple chains
- Selecting which variables are plotted
- Selecting what plots to show
- Giving a recording option
- Additional fine tuning features like
- Thinning
- Creating a buffer to limit the viewing
- Using a color mapping given some statistics
- Allow to apply transformation before plotting
Small example:
using Turing
using Turkie
using GLMakie # You could also use CairoMakie or another backend
@model function demo(x) # Some random Turing model
m0 ~ Normal(0, 2)
s ~ InverseGamma(2, 3)
m ~ Normal(m0, √s)
for i in eachindex(x)
x[i] ~ Normal(m, √s)
end
end
xs = randn(100) .+ 1 # Create some random data
m = demo(xs) # Create the model
cb = TurkieCallback(m) # Create a callback function to be given to the sample
chain = sample(m, NUTS(0.65), 300; callback = cb) # Sample and plot at the same timeIf you want to show only some variables you can give a Dict to TurkieCallback :
cb = TurkieCallback(
(m0 = [:trace, :mean], s = [:autocov, :var])
)
You can also directly pass OnlineStats object:
using OnlineStats
cb = TurkieCallback(
(v = [Mean(), AutoCov(20)],)
)If you want to record the video do
using Makie
record(cb.figure, joinpath(@__DIR__, "video.webm")) do io
addIO!(cb, io)
sample(m, NUTS(0.65), 300; callback = cb)
end