# How to use ParetoSmooth.jl

**URL:** https://discourse.julialang.org/t/how-to-use-paretosmooth-jl/79493
**Category:** Probabilistic Programming
**Tags:** turing, paretosmooth
**Created:** [April 14, 2022, 2:40pm UTC](https://discourse.julialang.org/t/how-to-use-paretosmooth-jl/79493 "2022-04-14T14:40:04Z")
**Posts on this page:** 1
**Showing post:** 2

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### Author: ![sethaxen](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/sethaxen/32/35604_2.png) [@sethaxen](https://discourse.julialang.org/u/sethaxen)
#### Post date: [April 19, 2022, 8:47am UTC](https://discourse.julialang.org/t/how-to-use-paretosmooth-jl/79493/2 "2022-04-19T08:47:06Z")

</div>

> [@mthelm85](#):
>
> ```julia
> ch = sample(test(Xₜ, Yₜ, 1), IS(), 1_000) # Assuming I need to use IS() here
> 
> ```

LOO is completely independent of the inference method, so no need to use `IS()` here. In fact, `IS()` seems to have some problems, so let’s use `NUTS()` instead.

You can retrieve the likelihood matrix either with `Turing.pointwise_loglikelihoods` or with `ParetoSmooth.pointwise_log_likelihoods`, which formats the matrix as needed by ParetoSmooth. But there’s also a convenience function that computes the log-likelihoods for you:

```julia
julia> ch = sample(test(Xₜ, Yₜ, 1), NUTS(), MCMCThreads(), 1_000, 4);
┌ Info: Found initial step size
└ ϵ = 0.21250000000000002
┌ Info: Found initial step size
└ ϵ = 0.30000000000000004
┌ Info: Found initial step size
└ ϵ = 0.225
┌ Info: Found initial step size
└ ϵ = 0.0125
Sampling (4 threads) 100%|█████████████████████████████████████████████████████████████████| Time: 0:00:00

julia> psis_loo(test(Xₜ, Yₜ, 1), ch)
[ Info: No source provided for samples; variables are assumed to be from a Markov Chain. If the samples are independent, specify this with keyword argument `source=:other`.
┌ Warning: Some Pareto k values are extremely high (>1). PSIS will not produce consistent estimates.
└ @ ParetoSmooth ~/.julia/packages/ParetoSmooth/A6x5U/src/InternalHelpers.jl:43
Results of PSIS-LOO-CV with 4000 Monte Carlo samples and 1 data points. Total Monte Carlo SE of 0.37.
┌───────────┬───────┬──────────┬───────┬─────────┐
│ │ total │ se_total │ mean │ se_mean │
├───────────┼───────┼──────────┼───────┼─────────┤
│ cv_elpd │ 5.65 │ NaN │ 5.65 │ NaN │
│ naive_lpd │ 8.19 │ NaN │ 8.19 │ NaN │
│ p_eff │ 2.53 │ NaN │ 2.53 │ NaN │
└───────────┴───────┴──────────┴───────┴─────────┘

```

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