Problem fitting a State-space model with matrices in RxInfer.jl

Hi everyone,

I am trying to recreate a paper from J. Luttinen as follows . However, I am having some difficulties to infer A and C matrices due to following reason:

It comes from a model that is specified as such:

@model function lssm(N, D, M)

x = randomvar(N)
y = datavar(Vector{Float64}, N)

x_prior ~ MvNormalMeanCovariance(zeros(D), 0.01*diageye(D))
x_prev = x_prior

α ~ InverseWishart(D^2, 1.0e-5*diageye(D))
γ ~ InverseWishart(M*D, 1.0e-5*diageye(M))
τ ~ InverseWishart(M^2, 1.0e-5*diageye(M))

A ~ MatrixNormal(zeros(D, D), α, diageye(D))
C ~ MatrixNormal(zeros(M, D), γ, diageye(D))

for i in 1:N
    x[i] ~ MvNormalMeanCovariance(A * x_prev, diageye(D))
    y[i] ~ MvNormalMeanCovariance(C * x[i], τ) 
    x_prev = x[i]


Is there any possible alternative that I could try? Thank you.