# MCMC Sampling Method Overview/Comparison?

**URL:** <https://discourse.julialang.org/t/mcmc-sampling-method-overview-comparison/88496>\
**Category:** Statistics\
**Created:** [October 9, 2022, 7:58pm UTC](https://discourse.julialang.org/t/mcmc-sampling-method-overview-comparison/88496 "2022-10-09T19:58:52Z")\
**Posts on this page:** 1\
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**Author:** ![Alec\_Loudenback](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/alec_loudenback/32/278_2.png) [@Alec\_Loudenback](https://discourse.julialang.org/u/Alec_Loudenback)\
**Post date:** [October 11, 2022, 1:49am UTC](https://discourse.julialang.org/t/mcmc-sampling-method-overview-comparison/88496/2 "2022-10-11T01:49:27Z")

</div>

Another question: does Turing support sampling on GPU (e.g. CUDA)? I have played around with it but not been successful (e.g. sample below).

However, [posts like this one](https://discourse.julialang.org/t/gpu-support-for-turing-modeling-with-system-of-odes/77410/2) seem to indicate that it’s possible (and should just work out of the box?).

> **Attempted CUDA code**
>
> ```julia
> @model function model_gaussian2(claims,n)
> μ ~ Normal(0.05,0.1)
> σ ~ Exponential(0.25)
> 	
> claims .~ Binomial.(n,logistic.(μ))
> end
> 
> ```
> 
> where `claims` and `n` are `CuArrays` of integers:
> 
> ```julia
> mg = model_gaussian2(CuArray(claims_summary.claims),CuArray(claims_summary.n))
> cg = sample(mg, NUTS(), 500)
> 
> ```
> 
> Resutls in:
> 
> ```julia
> InvalidIRError: compiling kernel #broadcast_kernel#17(CUDA.CuKernelContext, CUDA.CuDeviceVector{Float64, 1}, Base.Broadcast.Broadcasted{CUDA.CuArrayStyle{1}, Tuple{Base.OneTo{Int64}}, typeof(StatsAPI.loglikelihood), Tuple{Base.Broadcast.Extruded{CUDA.CuDeviceVector{Distributions.Binomial{Float64}, 1}, Tuple{Bool}, Tuple{Int64}}, Base.Broadcast.Extruded{CUDA.CuDeviceVector{Int64, 1}, Tuple{Bool}, Tuple{Int64}}}}, Int64) resulted in invalid LLVM IR
> 
> Reason: unsupported call through a literal pointer (call to .text)
> 
> ```

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