Make the 99th perc default constructor a censored_pmf feature

EpiAware/src/EpiAwareUtils/censored_pmf.jl

@@ -50,20 +50,29 @@ for double censored delays is then found using simple differencing on the CDF.
 Arguments:
 - `dist`: The distribution from which to create the PMF.
 - `Δd`: The step size for discretizing the domain. Default is 1.0.
-- `D`: The upper bound of the domain. Must be greater than `Δd`.
+- `D`: The upper bound of the domain. Must be greater than `Δd`. Default `D = nothing`
+indicates that the distribution should be truncated at its 99th percentile rounded
+to nearest multiple of `Δd`.
 
 Returns:
 - A vector representing the PMF.
 
 Raises:
 - `AssertionError` if the minimum value of `dist` is negative.
 - `AssertionError` if `Δd` is not positive.
-- `AssertionError` if `D` is not greater than `Δd`.
+- `AssertionError` if `D` is shorter than `Δd`.
+- `AssertionError` if `D` is not a multiple of `Δd`.
 """
-function censored_pmf(dist::Distribution; Δd = 1.0, D)
+function censored_pmf(dist::Distribution; Δd = 1.0, D = nothing)
     @assert minimum(dist)>=0.0 "Distribution must be non-negative."
     @assert Δd>0.0 "Δd must be positive."
-    @assert D>Δd "D must be greater than Δd."
+
+    if isnothing(D)
+        x_99 = invlogcdf(dist, log(0.99))
+        D = round(Int64, x_99 / Δd) * Δd
+    end
+
+    @assert D>=Δd "D can't be shorter than Δd."
 
     ts = 0.0:Δd:D |> collect
 

EpiAware/src/EpiObsModels/LatentDelay.jl

@@ -13,8 +13,8 @@ observed data.
     where {M <: AbstractTuringObservationModel, C <: ContinuousDistribution}`: Constructs
     a `LatentDelay` object with the given underlying observation model and continuous
     distribution. The `D` parameter specifies the right truncation of the distribution,
-    with default `D = nothing` indicating that the distribution should be truncated at its
-    99th percentile rounded to nearest integer. The `Δd` parameter specifies the
+    with default `D = nothing` indicates that the distribution should be truncated
+    at its 99th percentile rounded to nearest multiple of `Δd`. The `Δd` parameter specifies the
     width of each delay interval.
 
 - `LatentDelay(model::M, pmf::T) where {M <: AbstractTuringObservationModel, T <: AbstractVector{<:Real}}`: Constructs a `LatentDelay` object with the given underlying observation model and delay PMF.
@@ -35,9 +35,6 @@ struct LatentDelay{M <: AbstractTuringObservationModel, T <: AbstractVector{<:Re
     function LatentDelay(model::M, distribution::C; D = nothing,
             Δd = 1.0) where {
             M <: AbstractTuringObservationModel, C <: ContinuousDistribution}
-        if isnothing(D)
-            D = invlogcdf(distribution, log(0.99)) |> round
-        end
         pmf = censored_pmf(distribution; Δd = Δd, D = D)
         return LatentDelay(model, pmf)
     end

EpiAware/test/EpiAwareUtils/censored_pmf.jl

@@ -45,4 +45,14 @@
         dist = Exponential(1.0)
         @test_throws AssertionError censored_pmf(dist, Δd = 1.0, D = 3.5)
     end
+
+    @testset "Test case 7: testing default choice of D" begin
+        dist = Exponential(1.0)
+        pmf = censored_pmf(dist, Δd = 1.0)
+        #Check the normalisation constant is > 0.99 for analytical solution
+        expected_pmf_uncond = [exp(-1)
+                               [(1 - exp(-1)) * (exp(1) - 1) * exp(-s)
+                                for s in 1:length(pmf)]]
+        @test sum(expected_pmf_uncond) > 0.99
+    end
 end