# 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\
**Showing post:** 3

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**Author:** ![torfjelde](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/torfjelde/32/206542_2.png) [@torfjelde](https://discourse.julialang.org/u/torfjelde)\
**Post date:** [May 7, 2020, 5:29pm UTC](https://discourse.julialang.org/t/is-there-a-turing-alternative-to-pm-deterministic-from-pymc3/38667/3 "2020-05-07T17:29:00Z")

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As @trappmartin says, you need to define a transformation from unconstrained to constrained, but in this case that’s just going to be the identity operation, so you only the need additional bit of code:

```julia
Bijectors.bijector(d::Determin) = Identity{0}() # <= `0` indicates 0-dimensional, i.e. univariate 

```

and you’ll be able to use HMC and NUTS:)

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