# Implementing Gibbs sampler for ODE parameter estimation

**URL:** <https://discourse.julialang.org/t/implementing-gibbs-sampler-for-ode-parameter-estimation/99132>\
**Category:** Probabilistic Programming\
**Tags:** question, turing, sciml\
**Created:** [May 19, 2023, 6:47pm UTC](https://discourse.julialang.org/t/implementing-gibbs-sampler-for-ode-parameter-estimation/99132 "2023-05-19T18:47:40Z")\
**Posts on this page:** 3\
**Page:** 1

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**Author:** ![PeX](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/pex/32/49986_2.png) [@PeX](https://discourse.julialang.org/u/PeX)\
**Post date:** [May 19, 2023, 6:47pm UTC](https://discourse.julialang.org/t/implementing-gibbs-sampler-for-ode-parameter-estimation/99132/1 "2023-05-19T18:47:40Z")

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I would like to use a Gibbs sampler for a model that looks like this (in Turing.jl):

```julia
@model function fit_burger2(data::AbstractVector, prob)
    # Prior distributions
    σᵣ = 1.5e6
	Aₘ ~ truncated(Normal(0.1, 0.05); lower=0.01, upper=1.1)
        Qₘ ~ Uniform(1e3,1e6)
	n ~ Uniform(0.7,4.5)
	pₘ ~ truncated(Normal(0.36, 0.2); lower=0.3, upper=0.7)
	Eₖ ~ Uniform(1e9,1e10)
	Eₘ ~ Uniform(1e8,9e9)
	η ~ truncated(Normal(1e11, 0.5e9); lower=1e9, upper=1e12)
	dₛₛ ~ truncated(Normal(2.4e-4, 1e-4); lower=1e-5, upper=1e-3)
	Fₛₛ ~ truncated(Normal(0.5, 0.1); lower=0.3, upper=0.7)
	ε₁ ~ Uniform(0.01,1.0)
	ε₂ ~ truncated(Normal(0.16, 0.07); lower=0.01, upper=0.9)
	S = 1e-4
	R = 8.3145
	T = 263

    # Simulate the model 
    p = [Aₘ, Qₘ, n, pₘ, R, T, Eₖ, Eₘ, η, dₛₛ, Fₛₛ, ε₁, ε₂, S]
    predicted = solve(prob, Rosenbrock23(); p=p, saveat=df2.time, save_idxs=3)

    # Observations
	data ~ MvNormal(predicted.u, σᵣ^2 * I)

    return nothing
end

```

I wonder what would be the process to do it, currently, I’m using regular Metropolis-Hastings:

```julia
chain2 = sample(model3, MH(), MCMCSerial(), 120_000, 2; 
										progress=true,
										discard_initial=70_000,
										thinning=100)

```

I was trying to find examples/tutorials that use Gibbs for estimating ODE parameters but couldn’t find any. So any hint to throw me in the right direction would be highly appreciated 🙂

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<div class="post-metadata">

**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 20, 2023, 1:32pm UTC](https://discourse.julialang.org/t/implementing-gibbs-sampler-for-ode-parameter-estimation/99132/2 "2023-05-20T13:32:09Z")

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What sort of Gibbs sampling are you thinking?🙂

If you’re thinking Metropolis-within-Gibbs, this might be helpful: [Guide](https://turinglang.org/v0.25/docs/using-turing/guide#compositional-sampling-using-gibbs)

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<div class="post-metadata">

**Author:** ![PeX](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/pex/32/49986_2.png) [@PeX](https://discourse.julialang.org/u/PeX)\
**Post date:** [May 20, 2023, 8:58pm UTC](https://discourse.julialang.org/t/implementing-gibbs-sampler-for-ode-parameter-estimation/99132/3 "2023-05-20T20:58:14Z")

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I was wondering if there is a way to update one parameter at a time while sampling, holding the others constant, and see what happens to the likelihood. Instead of updating the entire parameter set every iteration, just update one at a time (like in Gibbs sampling). The link you pointed out to shows how to treat each parameter separately, but not exactly what I need for the ODE problem.