MonteCarloMeasurements.jl
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Propagation of distributions by Monte-Carlo sampling: Real number types with uncertainty represented by samples.
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This PR adds a type CuParticles that works as regular Particles but is backed by a CuArray. Expect in the range of 5-7x speedup over Particles if the sample size...
An idea using cassette or irtools: specify the function that is difficult, execute in a difficultContext and when the difficult function is called, change behavior of all particle methods to...
Hi @baggepinnen , For some Soss work, I have something like (simplified example) ```julia julia> a = [Particles(), Particles()+1, Particles()+2] 3-element Array{Particles{Float64,2000},1}: -5.33e-18 ± 1.0 1.0 ± 1.0 2.0 ±...
The show method seems to to some rounding with particles in an array: ```julia julia> [10000+i ± 0.5 for i=1:10:50] 5-element Array{Particles{Float64,2000},1}: 10000.0 ± 0.5 10000.0 ± 0.5 10000.0 ±...
This seems inconsistent: ``` julia> NaN ± NaN ERROR: ArgumentError: Normal: the condition σ >= zero(σ) is not satisfied. julia> NaN ± Inf Particles{Float64,2000} NaN ± NaN ```
I've been thinking about about the approach for `WeightedParticles`. When I've needed this I haven't been able to use the built-in type. As it stands, the weighting vector is per-`Particles`,...
Thanks for the great package that Chad Scherrer introduced me to! Currently, ```julia julia> Bernoulli(Particles(10, Uniform())) ERROR: TypeError: non-boolean (Particles{Bool,10}) used in boolean context ``` is an error due to...
Hi @baggepinnen , we've talked before about StructArrays... I haven't used this package much before, but I did get this going and it seems to work well: ```julia julia> using...