Abstract
The previous chapter presented the slice sampler, a special case of a Markov chain algorithm that did not need an Accept–Reject step to be valid, seemingly because of the uniformity of the target distribution. The reason why the slice sampler works is, however, unrelated to this uniformity and we will see in this chapter a much more general family of algorithms that function on the same principle. This principle is that of using the true conditional distributions associated with the target distribution to generate from that distribution.
All that mattered was that slowly, by degrees, by left and right then left and right again, he was guiding them towards the destination.
—Ian Rankin, Hide and Seek
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Notes
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Robert, C.P., Casella, G. (2004). The Two-Stage Gibbs Sampler. In: Monte Carlo Statistical Methods. Springer Texts in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4757-4145-2_9
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DOI: https://doi.org/10.1007/978-1-4757-4145-2_9
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