BayesHistogram.BayesHistogram
— ModuleBayesHistogram.jl main procedure: function bayesianblocks( t::AbstractVector{T}; weights::AbstractVector{W} = one.(t), sumw2::AbstractVector{W} = abs2.(weights), prior = BIC(), resolution = Inf, mincounts::Real = 0, ) where {T<:Real,W<:Real}
BayesHistogram.bayesian_blocks
— Methodfunction bayesian_blocks(
datas::AbstractVector{T};
weights::AbstractVector{W} = one.(t),
sumw2::AbstractVector{W} = abs2.(weights),
prior = BIC(),
resolution = Inf,
min_counts::Real = 0,
) where {T<:Real,W<:Real}
datas
: Observationsweights
: sample weight of each observationsumw2
: sum of weight^2 in each observation, this is particularly useful
when this algorihtm is used for re-binning of already made histograms where the sumw2
for each bin is different from weight^2
of each bin.
prior
: choose fromNoPrior
,Pearson
,Geometric
,BIC
,AIC
,HQIC
,FPR
,Scargle
(identical toFPR
)- resolution: handles on how fine we count along the `datas axis
- min_counts: minimum sum of weights of a block that can be splitted.