A parameter-expanded spike-and-slab prior constrained to the stationary region, combined with a mixture G-Wishart prior, enables Bayesian inference of sparse stable VAR processes via Metropolis-within-Gibbs sampling.
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Bayesian inference of sparsity in stable vector autoregressive processes
A parameter-expanded spike-and-slab prior constrained to the stationary region, combined with a mixture G-Wishart prior, enables Bayesian inference of sparse stable VAR processes via Metropolis-within-Gibbs sampling.