I was attempting to define a mamba model with a multivariate normal distribution for the data:
model = Model(
PMT = Stochastic(1,
(mu,sigma) -> MvNormal(mu,sigma),
false),
mu = Logical(1,
(x,y) -> makeMeans(x,y)
),
sigma = Logical(2 ,
(x,y) -> makeCov(x,y)
),
x = Stochastic(
() -> Normal(0,1)
),
y = Stochastic(
() -> Normal(0,1)
)
)
makeMeans and makeCov return a length 4 vector and a 4x4 matrix.
I get the following error when trying to run mcmc:
ERROR: MethodError: no method matching PDMats.PDMat(::ArrayLogical{2})
Closest candidates are:
PDMats.PDMat(::AbstractMatrix, ::Cholesky) at ~/.julia/packages/PDMats/HCniD/src/pdmat.jl:12
PDMats.PDMat(::Cholesky) at ~/.julia/packages/PDMats/HCniD/src/pdmat.jl:21
PDMats.PDMat(::Symmetric) at ~/.julia/packages/PDMats/HCniD/src/pdmat.jl:20
I can run mcmc if I replace sigma with a scalar, so it seems it's an issue with using a matrix for the covariance.
I was attempting to define a mamba model with a multivariate normal distribution for the data:
makeMeans and makeCov return a length 4 vector and a 4x4 matrix.
I get the following error when trying to run mcmc:
I can run mcmc if I replace sigma with a scalar, so it seems it's an issue with using a matrix for the covariance.