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What is an intuitive explanation of hyper-rectangle slice sampling?
How does hyper-rectangle slice sampling compare to other MCMC methods?
Hyper-rectangle slice sampling is often more efficient than other MCMC methods, such as Metropolis-Hastings, in high-dimensional spaces because it is able to explore the space better by "slicing" it into more manageable pieces. However, it can be computationally expensive since it requires calculating the probability density function multiple times.