Comments (4)
I think I can take a look at this, maybe together with @VasylHafych, to harmonize our efforts from marginal mode and plotting in this new type.
from bat.jl.
Thanks! We can split _auto_binning_nbins
among separate subtypes of AbstractBinningAlgorithm
, and have a FixedBinning
too or so. Of course we should allow AbstractArray
s and NamedTuple
s of binning algos (like you already implemented for plotting) so the user can specify binning in a per-parameter fashion.
from bat.jl.
Ok, so just to make sure I understand what you propose:
We will add a AbstractBinningAlgorithm
as a supertype for the different automatic binnings, like Freedman–Diaconis
and so on.
These types could then be passed to plot
and bat_marginalize
as a keyword argument like plot(samples, :a, bins=FD())
and would trigger the right version of _auto_binning_nbins
.
But how exactly should we handle user-defined binning, like fixed number of bins or bin edges. Should it be possible to pass them directly as Ints and Ranges, like currently in plotting: plot(samples, bins=(200, -1:.01:1))
?
Or would these need to be put into a subtype like FixedBinning
, so that plot(samples, bins = FixedBinning(200, -1:0.1:1))
?
from bat.jl.
Yes, that's the idea. FixedBinning
would indeed be a subtype of AbstractBinningAlgorithm
.
To make life more comfortable for the users, we should define Base.conv(::Type{AbstractBinningAlgorithm}, x)
for symbols (e.g. :fd
gets converted to Freedman–Diaconis()
), scalars (like 500
means 500 bins, ends of range auto-adjusted) and ranges (0:0.2:1000
will be converted to an instance of FixedBinning
).
from bat.jl.
Related Issues (20)
- Linear Regression with BAT HOT 4
- Type instability in bat_sample HOT 1
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- Pullback of DistributionTransform HOT 7
- Sampling with a single chain
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- Handling periodic parameters HOT 3
- Use histogram as both prior and input data? HOT 1
- MLE best-fit parameter disagreeing with `pyhf` HOT 15
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- Custom CIs and/or printing all CI values via bat_report HOT 8
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from bat.jl.