  Models which include domain constraints occur in myriad contexts such as
econometrics, genomics, and environmetrics, though simulating from constrained
distributions can be computationally expensive. In particular, repeated
sampling from constrained distributions is a common task in Bayesian
inferential methods, where coping with these constraints can cause troublesome
computational burden. Here, we introduce computationally efficient methods to
make exact and independent draws from both the multivariate normal and Wishart
distributions with box constraints. In both cases, these variables are sampled
using a direct algorithm. By substantially reducing computing time, these new
algorithms improve the feasibility of Monte Carlo-based inference for
box-constrained, multivariate normal and Wishart distributions.
