# Turing: no method matching MvNormal(::Matrix{Float64}, ::Float64)
**URL:** https://discourse.julialang.org/t/turing-no-method-matching-mvnormal-matrix-float64-float64/69207
**Category:** Statistics
**Tags:** question
**Created:** [October 4, 2021, 8:00pm UTC](https://discourse.julialang.org/t/turing-no-method-matching-mvnormal-matrix-float64-float64/69207 "2021-10-04T20:00:22Z")
**Posts on this page:** 5
**Page:** 1
### Author:  [@Tarotis](https://discourse.julialang.org/u/Tarotis)
#### Post date: [October 4, 2021, 8:00pm UTC](https://discourse.julialang.org/t/turing-no-method-matching-mvnormal-matrix-float64-float64/69207/1 "2021-10-04T20:00:22Z")
Hello everybody,
as I want to switch from R to Julia for my Bayesian modelling, tonight I run my first models in Turing. However, I encountered some problems, principally when trying to model a random-intercept model.
The model is defined as follows:
```julia
@model my_regression(x, y, idx) = begin
α ~ Normal(mean(y), 2.5 * std(y))
β ~ Exponential(2)
σ ~ Exponential(1 / std(y))
n_gr = length(unique(idx))
τ ~ truncated(Cauchy(0, 1), 0, Inf) # group-level SDs intercepts
αⱼ ~ filldist(Normal(0, τ), n_gr) # group-level intercepts
ŷ = α .+ αⱼ[idx] .+ x * β
y ~ MvNormal(ŷ, σ)
end;
```
However, when running the model I receive the error `no method matching MvNormal(::Matrix{Float64}, ::Float64)`.
A similar model without the group-level intercepts runs without any problems and uses ` @. y ~ Normal(exp(α + β1 * x), σ)` as a linear model. I suspect that a main reason for my problems could be due to a lack of understanding of the role of broadcasting, but I am really not sure.
Looking forward to your comments,
Tarotis
---
### Author:  [@DoktorMike](https://discourse.julialang.org/u/DoktorMike)
#### Post date: [October 4, 2021, 8:29pm UTC](https://discourse.julialang.org/t/turing-no-method-matching-mvnormal-matrix-float64-float64/69207/2 "2021-10-04T20:29:24Z")
I could be wrong but i think MvNormal is expecting a vector and not a matrix as you appear to be sending it.
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### Author:  [@Christopher\_Fisher](https://discourse.julialang.org/u/Christopher_Fisher)
#### Post date: [October 4, 2021, 8:56pm UTC](https://discourse.julialang.org/t/turing-no-method-matching-mvnormal-matrix-float64-float64/69207/3 "2021-10-04T20:56:31Z")
You are correct. ŷ should be a vector. I’m guessing `x` is a n by 1 array rather than a column vector.
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### Author:  [@sethaxen](https://discourse.julialang.org/u/sethaxen)
#### Post date: [October 4, 2021, 10:01pm UTC](https://discourse.julialang.org/t/turing-no-method-matching-mvnormal-matrix-float64-float64/69207/4 "2021-10-04T22:01:35Z")
What the others have pointed out is correct. There is a `MatrixNormal`, but that’s likely overkill here. This should be fine:
```julia
y ~ MvNormal(vec(ŷ), σ)
```
Edit: this assumes you also vectorize `y`.
Also, you’ll get better performance if you precompute constants like `mean(y)`, `std(y)`, and `unique(idx)` outside of the model. And this is faster:
```julia
ŷ = α .+ view(αⱼ, idx) .+ x .* β
```
---
### Author:  [@Tarotis](https://discourse.julialang.org/u/Tarotis)
#### Post date: [October 5, 2021, 8:06am UTC](https://discourse.julialang.org/t/turing-no-method-matching-mvnormal-matrix-float64-float64/69207/5 "2021-10-05T08:06:22Z")
Thank you all for the feedback and explicit explanations! It works great now. As the models will be quite complex in the end I fear that STAN/brms might be faster still, but I guess I will only find out once I have the corresponding models ready.