`BayesHistogram.BayesHistogram`

— ModuleBayesHistogram.jl main procedure: function bayesian*blocks( t::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}

`BayesHistogram.bayesian_blocks`

— Method```
function 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`

: Observations`weights`

: sample weight of each observation`sumw2`

: 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 from`NoPrior`

,`Pearson`

,`Geometric`

,`BIC`

,`AIC`

,`HQIC`

,`FPR`

,`Scargle`

(identical to`FPR`

)- resolution: handles on how fine we count along the `datas axis
- min_counts: minimum sum of weights of a block that can be splitted.