# Is there a turing alternative to pm.Deterministic from pymc3?

**URL:** <https://discourse.julialang.org/t/is-there-a-turing-alternative-to-pm-deterministic-from-pymc3/38667>\
**Category:** New to Julia\
**Tags:** turing, distributions\
**Created:** [May 3, 2020, 11:58am UTC](https://discourse.julialang.org/t/is-there-a-turing-alternative-to-pm-deterministic-from-pymc3/38667 "2020-05-03T11:58:42Z")\
**Posts on this page:** 1\
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**Author:** ![trappmartin](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/trappmartin/32/1165_2.png) [@trappmartin](https://discourse.julialang.org/u/trappmartin)\
**Post date:** [May 5, 2020, 8:55am UTC](https://discourse.julialang.org/t/is-there-a-turing-alternative-to-pm-deterministic-from-pymc3/38667/2 "2020-05-05T08:55:35Z")

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Hi,  
You can do something like this:

```julia
using Turing, Random

# Define a deterministic distribution
struct Determin{T<:Real} <: ContinuousUnivariateDistribution
  val::T
end

Distributions.rand(rng::AbstractRNG, d::Determin) = d.val
Distributions.logpdf(d::Determin, x::T) where T<:Real = zero(x)

@model function testmodel(x)
  y ~ Normal()
  m ~ Determin(y * 2)
  for i in eachindex(x)
    x[i] ~ Normal(m)
  end
end

# instantiate a model
model = testmodel(randn(10))

# inference
chain = sample(model, SMC(), 1000)

```

And if I would recall how to properly define the bijection for a custom distribution, you could also use HMC or NUTS. @torfjelde could you jump in?

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