Clarification needed on error handling when model is misspecified

[albertpod]

In the context of the discussion, I encountered an unclear error message while working with an SSM. Below is a simplified version of the code that produces the error:

@model function example_error(y)
    x0 ~ Normal(μ=0, γ=1)
    for i in eachindex(y)
        x[i] ~ Normal(μ=x0, γ=1)
        y[i] ~ Normal(μ=x, γ=1)
        x0 = x[i]
    end
end

infer(
    model = example_error(),
    data = (y = randn(100), ),
    showprogress = true
)

This code results in the following error message:

ERROR: Don't know how to alias interface μ in 3 for NormalMeanPrecision

Thanks to @bvdmitri, I realized that the model was incorrectly specified. In the problematic line:

y[i] ~ Normal(μ=x, γ=1)

I mistakenly used x instead of x[i]. The correct model, which addresses this error, is as follows:

@model function example_error(y)
    x0 ~ Normal(μ=0, γ=1)
    for i in eachindex(y)
        x[i] ~ Normal(μ=x0, γ=1)
        y[i] ~ Normal(μ=x[i], γ=1)
        x0 = x[i]
    end
end

It would be beneficial if the error message could be more descriptive to pinpoint mistakes such as referencing a whole array (x) instead of an array element (x[i]). A suggested error message might be:

ERROR: Incorrect variable reference. Expected an array element (e.g., 'x[i]'), but found an array ('x'). Please check your variable indices.

[bvdmitri]

Thanks for posting @albertpod !
Using x as an entire array is technically okay, which is what we do in mixture nodes. I think a more practical error message for this scenario could be something like:

Error: `NormalMeanPrecision` expects 2 input arguments, but 3 have been provided `(μ = x[1], μ = x[2], γ=1)`.

or something similar. What do you think?

[albertpod]

Yes, that's a better error message indeed.