ReactiveMP: How to run linear model with multiple predictors and an intercept

Specific Domains Probabilistic programming
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2

This works (runs at least, haven’t tested thoroughly)

@model function linear_regression(n,m)
    a ~ MvNormalMeanCovariance(zeros(m), diagm(ones(m)))
    b ~ NormalMeanVariance(0.0,1.0)
    c ~ ones(n)*b
    x = datavar(Matrix{Float64})
    y = datavar(Vector{Float64})
    z ~ x*a+c
    y ~ MvNormalMeanCovariance(z , tiny .*diagm(ones(n)))

    return a, y
end
results = inference(
    model = Model(linear_regression, n,m),
    data  = (y = randn(n), x = randn(n,m)),
    returnvars   = (a = KeepLast(),),
    iterations   = 20
);

Guess I just need to get my head around the datavar/randomvar as vectors/matrices vs vectors/matrices of datavars/randomvars, and nrush up on very basic linear algebra!