# \[ANN\]: RxInfer.jl 2.0 Julia package for automated Bayesian inference on a factor graph with reactive message passing

**URL:** https://discourse.julialang.org/t/ann-rxinfer-jl-2-0-julia-package-for-automated-bayesian-inference-on-a-factor-graph-with-reactive-message-passing/89879
**Category:** Package Announcements
**Tags:** reactiveprogramming, distributions, bayesian-inference, reactivemp, rxinfer
**Created:** [November 7, 2022, 1:49pm UTC](https://discourse.julialang.org/t/ann-rxinfer-jl-2-0-julia-package-for-automated-bayesian-inference-on-a-factor-graph-with-reactive-message-passing/89879 "2022-11-07T13:49:56Z")
**Posts on this page:** 1
**Showing post:** 12

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### Author: ![bvdmitri](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/bvdmitri/32/22935_2.png) [@bvdmitri](https://discourse.julialang.org/u/bvdmitri)
#### Post date: [November 19, 2022, 11:42am UTC](https://discourse.julialang.org/t/ann-rxinfer-jl-2-0-julia-package-for-automated-bayesian-inference-on-a-factor-graph-with-reactive-message-passing/89879/12 "2022-11-19T11:42:33Z")

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Hey @stanlazic !

Thank you for the interest in `RxInfer`. For your example to work you need to slightly modify your model and input data in the following way:

```julia
# define model
@model function linear_regression(n)
    # I changed x to be of the vector type
    x = datavar(Vector{Float64}, n)
    y = datavar(Float64, n)

    # priors
    β0 ~ NormalMeanVariance(1.0, 100.0)
    # I changed beta to be of the multivariate type
    β ~ MvNormalMeanCovariance([0.0, 0.0], [1.0 0.0; 0.0 1.0])
    
    for i in 1:n       
        y[i] ~ NormalMeanVariance(dot(x[i], β) + β0, 1.0)
    end
end

```

and the inference:

```julia
xdata = collect.(zip(xdata1, xdata2)) # collect into pairs

results = inference(
    model = linear_regression(length(xdata1)), 
    data = (y = ydata, x = xdata), 
    initmessages = (β0 = NormalMeanVariance(0.0, 1.0), )
)

```

and the results will be available:

```julia
mean_cov(results.posteriors[:β])

```

```julia
([1.1638631582255687, 2.1389045170632293], [0.24982928559123693 0.07513981828561604; 0.07513981828561604 0.21513388551908566])

```

The reason for your error is that `dot` is not defined between two vectors of `RandomVariable`s, but rather between two multivariate `RandomVariable`s.

You may also be interested in the following example: [ReactiveMP: How to run linear model with multiple predictors and an intercept - #6 by albertpod](https://discourse.julialang.org/t/reactivemp-how-to-run-linear-model-with-multiple-predictors-and-an-intercept/85290/6). This example uses old version of the `RxInfer`, which we called `ReactiveMP`. But the example itself should work in any case if you remove `[default_factorisation = MeanField()] ` from the `@model` specification and put it in the `inference()` function as follows:

```julia
n = 250
m = 100

@model function multivariate_linear_regression(n,m)
    a ~ MvNormalMeanCovariance(zeros(m), diagm(ones(m)))
    b ~ NormalMeanVariance(0.0,1.0)
    W ~ InverseWishart(n+2, diageye(n))
    c ~ ones(n)*b
    x = datavar(Matrix{Float64})
    y = datavar(Vector{Float64})
    z ~ x*a+c
    y ~ MvNormalMeanCovariance(z, W)

end

results = inference(
    model = multivariate_linear_regression(n,m),
    data = (y = randn(n), x = randn(n,m)),
    initmarginals = (W = InverseWishart(n+2, diageye(n)), ),
    returnvars = (a = KeepLast(), b = KeepLast(), W = KeepLast()),
    free_energy = true,
    iterations = 10,
    constraints = MeanField()
)

```

---

_[View the full topic](https://discourse.julialang.org/t/ann-rxinfer-jl-2-0-julia-package-for-automated-bayesian-inference-on-a-factor-graph-with-reactive-message-passing/89879)._