# Custom likelihoods in Turing.jl

**URL:** <https://discourse.julialang.org/t/custom-likelihoods-in-turing-jl/16818>\
**Category:** General Usage\
**Created:** [October 26, 2018, 10:17am UTC](https://discourse.julialang.org/t/custom-likelihoods-in-turing-jl/16818 "2018-10-26T10:17:29Z")\
**Posts on this page:** 1\
**Showing post:** 5

<div class="post-metadata">

**Author:** ![Tamas\_Papp](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/tamas_papp/32/25949_2.png) [@Tamas\_Papp](https://discourse.julialang.org/u/Tamas_Papp)\
**Post date:** [October 26, 2018, 11:57am UTC](https://discourse.julialang.org/t/custom-likelihoods-in-turing-jl/16818/5 "2018-10-26T11:57:10Z")

</div>

`NUTS_init_tune_mcmc` returns the chain **and** the tuned sampler (so you could continue sampling). Very few people use that so I may remove it as the default, or introduce an alternative API. Currently you can just keep the first value, eg (continuing the above example)

```julia
using Random, Distributed
Nchains = 4
rngs = [Random.seed!(i) for i in 1:Nchains];
output = pmap(x -> first(NUTS_init_tune_mcmc(x, ∇P, 1000)), rngs)

```

works fine (diagnostic output is garbled though, as it is not ready for parallel chains yet).

Conversion to MCMCChain.jl is not supported by my packages (and I don’t know about others). I kind of prefer the bare-bones, modular approach. In practice, IMO the two most useful diagnostics are \hat{R} and ESS, both of which are supported by MCMCDiagnostics.

If you get errors, please make a self-contained MWE and I will look at it.

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