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docs/src/state_interface.md

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@@ -43,15 +43,13 @@ x_i &\sim \text{Normal}(\mu, \sqrt{\tau^2})
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We can write the log joint probability function as follows, where for the sake of simplicity for the following steps, we will assume that the `mu` and `tau2` parameters are one-element vectors. And `x` is the data.
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```@example gibbs_example
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using AbstractMCMC: AbstractMCMC, LogDensityProblems # hide
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using AbstractMCMC: AbstractMCMC # hide
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using LogDensityProblems # hide
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using Distributions # hide
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using Random # hide
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using AbstractMCMC: AbstractMCMC # hide
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using AbstractPPL: AbstractPPL # hide
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using BangBang: constructorof # hide
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```
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```@example gibbs_example
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function log_joint(; mu::Vector{Float64}, tau2::Vector{Float64}, x::Vector{Float64})
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# mu is the mean
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# tau2 is the variance

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