# How to draw simulated data from a given turing model given specific parameters

**URL:** <https://discourse.julialang.org/t/how-to-draw-simulated-data-from-a-given-turing-model-given-specific-parameters/83413>\
**Category:** Modelling & Simulations\
**Tags:** question, turing\
**Created:** [June 27, 2022, 4:19pm UTC](https://discourse.julialang.org/t/how-to-draw-simulated-data-from-a-given-turing-model-given-specific-parameters/83413 "2022-06-27T16:19:04Z")\
**Posts on this page:** 4\
**Page:** 1

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**Author:** ![neuro\_enthusiast](https://avatars.discourse-cdn.com/v4/letter/n/43a26b/32.png) [@neuro\_enthusiast](https://discourse.julialang.org/u/neuro_enthusiast)\
**Post date:** [June 27, 2022, 4:19pm UTC](https://discourse.julialang.org/t/how-to-draw-simulated-data-from-a-given-turing-model-given-specific-parameters/83413/1 "2022-06-27T16:19:05Z")

</div>

I was wondering how I can simulate data given parameter values using a turing model? Any help would be great!

ex.

```julia
using Turing, Distributions
@model function logistic_regression(x, y, n, σ)
    intercept ~ Normal(0, σ)

    student ~ Normal(0, σ)
    balance ~ Normal(0, σ)
    income ~ Normal(0, σ)

    for i in 1:n
        v = logistic(intercept + student * x[i, 1] + balance * x[i, 2] + income * x[i, 3])
        y[i] ~ Bernoulli(v)
    end
end;

```

I would like to generate y after setting student =1, balance = 1, intercept = 1, income = 1 and x = [1,1,1] without having to re-code the model.

---

<div class="post-metadata">

**Author:** ![jae0](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/jae0/32/37277_2.png) [@jae0](https://discourse.julialang.org/u/jae0)\
**Post date:** [June 28, 2022, 7:26pm UTC](https://discourse.julialang.org/t/how-to-draw-simulated-data-from-a-given-turing-model-given-specific-parameters/83413/2 "2022-06-28T19:26:03Z")

</div>

Don’t think you need Turing for that.

All your params are fixed, so just compute v.

Then,

```julia
using Distributions
rand( Bernoulli(v), 10) # or however many samples you want

```

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<div class="post-metadata">

**Author:** ![neuro\_enthusiast](https://avatars.discourse-cdn.com/v4/letter/n/43a26b/32.png) [@neuro\_enthusiast](https://discourse.julialang.org/u/neuro_enthusiast)\
**Post date:** [June 29, 2022, 12:47am UTC](https://discourse.julialang.org/t/how-to-draw-simulated-data-from-a-given-turing-model-given-specific-parameters/83413/3 "2022-06-29T00:47:23Z")

</div>

I’m just using Bayesian logistic regression as an example. My actual model is hierarchical. I’m trying to do parameter recovery from simulated data to check the fitting process. Life would be much easier if I can utilize the turing model for data simulation. Functions for prior predictive checks would help if you can direct me to them but ideally I’d like functions where I can simulate data with parameters that I chose. Thanks for the suggestion though.

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<div class="post-metadata">

**Author:** ![jae0](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/jae0/32/37277_2.png) [@jae0](https://discourse.julialang.org/u/jae0)\
**Post date:** [July 2, 2022, 3:04pm UTC](https://discourse.julialang.org/t/how-to-draw-simulated-data-from-a-given-turing-model-given-specific-parameters/83413/4 "2022-07-02T15:04:18Z")

</div>

ok, sounds like you want to use Turing’s predict function:

copied from their help:

```julia
julia> using Turing; Turing.setprogress!(false);
  [Info: [Turing]: progress logging is disabled globally
  
  julia> @model function linear_reg(x, y, σ = 0.1)
             β ~ Normal(0, 1)
  
             for i ∈ eachindex(y)
                 y[i] ~ Normal(β * x[i], σ)
             end
         end;
  
  julia> σ = 0.1; f(x) = 2 * x + 0.1 * randn();
  
  julia> Δ = 0.1; xs_train = 0:Δ:10; ys_train = f.(xs_train);
  
  julia> xs_test = [10 + Δ, 10 + 2 * Δ]; ys_test = f.(xs_test);
  
  julia> m_train = linear_reg(xs_train, ys_train, σ);
  
  julia> chain_lin_reg = sample(m_train, NUTS(100, 0.65), 200);
  ┌ Info: Found initial step size
  └ ϵ = 0.003125
  
  julia> m_test = linear_reg(xs_test, Vector{Union{Missing, Float64}}(undef, length(ys_test)), σ);
  
  julia> predictions = predict(m_test, chain_lin_reg)
  Object of type Chains, with data of type 100×2×1 Array{Float64,3}
  
  Iterations = 1:100
  Thinning interval = 1
  Chains = 1
  Samples per chain = 100
  parameters = y[1], y[2]
  
  2-element Array{ChainDataFrame,1}
  
  Summary Statistics
    parameters mean std naive_se mcse ess r_hat
    ────────── ─────── ────── ──────── ─────── ──────── ──────
          y[1] 20.1974 0.1007 0.0101 missing 101.0711 0.9922
          y[2] 20.3867 0.1062 0.0106 missing 101.4889 0.9903
  
  Quantiles
    parameters 2.5% 25.0% 50.0% 75.0% 97.5%
    ────────── ─────── ─────── ─────── ─────── ───────
          y[1] 20.0342 20.1188 20.2135 20.2588 20.4188
          y[2] 20.1870 20.3178 20.3839 20.4466 20.5895
  
  
  julia> ys_pred = vec(mean(Array(group(predictions, :y)); dims = 1));
  
  julia> sum(abs2, ys_test - ys_pred) ≤ 0.1
  true

```