Dear All

I am trying to implement Riemman Manifold Hamiltonian Monte Carlo algorithm
for sampling from robust linear mixed model's posterior, but to this end I
need the fisher information matrix for the full model and its inverse. This
is bayesian inference, so random effects are treated as free parameters and
they appear in the fisher matrix. Full model matrix (X and Z concatenated)
becomes collinear which (if I understand this correctly) makes the fisher
matrix non invertible. Is there some general solution to this problem? For
example, is there a parametrization of the linear mixed model that makes
the fisher information matrix (with random effects) invertible?

-- 
Borysław Paulewicz

bpaulewicz@swps.edu.pl
borys.paulewicz@gmail.com

Szkoła Wyższa Psychologii Społecznej
Wydział Zamiejscowy w Katowicach
ul. Kossutha 9
40-844 Katowice
www.swps.pl

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