# How do I distribute Mamba mcmc function iterations on different processors?

**URL:** <https://discourse.julialang.org/t/how-do-i-distribute-mamba-mcmc-function-iterations-on-different-processors/50462>\
**Category:** Performance\
**Tags:** question, package\
**Created:** [November 19, 2020, 8:52pm UTC](https://discourse.julialang.org/t/how-do-i-distribute-mamba-mcmc-function-iterations-on-different-processors/50462 "2020-11-19T20:52:58Z")\
**Posts on this page:** 1\
**Showing post:** 4

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**Author:** ![ElOceanografo](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/eloceanografo/32/624_2.png) [@ElOceanografo](https://discourse.julialang.org/u/ElOceanografo)\
**Post date:** [November 23, 2020, 7:48pm UTC](https://discourse.julialang.org/t/how-do-i-distribute-mamba-mcmc-function-iterations-on-different-processors/50462/4 "2020-11-23T19:48:08Z")

</div>

I’m not 100% sure what your model is–the code snippet you posted seems to only have one level. Do you think you could make up a [minimum working example](https://discourse.julialang.org/t/psa-make-it-easier-to-help-you/14757)? That said, it is definitely possible to do a (hierarchical) logistic regression in Turing. Here’s a basic example showing one way to do it:

```julia
using Turing
using Distributions

# define logistic (invlogit) function
logistic(x) = 1 / (1 + exp(-x))
# make up some fake parameters and data
npop = 2
ntime = 10
alpha_hyper = Normal(1, 2)
beta_hyper = Normal(0, 0.4)
alpha = rand(alpha_hyper, 1, 2)
beta = rand(beta_hyper, 1, 2)
x = randn(ntime, npop)
p = logistic.(alpha .* x .+ beta)
y = rand.(Bernoulli.(p))

@model function hierarchical_logistic_reg(x, y)
    # priors on hyperparameters
    mu_alpha ~ Normal(0, 5)
    sigma_alpha ~ Gamma(2, 3)
    mu_beta ~ Normal(0, 5)
    sigma_beta ~ Gamma(2, 3)
    # regression parameters
    alpha ~ Normal(mu_alpha, sigma_alpha)
    beta ~ Normal(mu_beta, sigma_beta)
    p = logistic.(alpha .* x .+ beta)
    # observation likelihood
    y ~ arraydist(Bernoulli.(p))
end

model = hierarchical_logistic_reg(x, y)
chn = sample(model, NUTS(), 1000)

using StatsPlots
plot(chn)

```

If you’re just starting out with Bayesian modeling in Julia, I’d probably recommend using Turing–it’s a more recent and more actively developed library at this point than Mamba.

One other point about doing distributed computations inside the model: double-check that it’s actually giving you a speedup compared to serial computation. For the example I gave above, the overhead of doing the parallel likelihood calculations actually make that approach slower than a simpler serial version.

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