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Undid revision 854756568 by 132.62.88.128 (talk) I don't understand what you mean. Can you explain (perhaps on talk page)? |
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In contrast to Metropolis, slice sampling automatically adjusts the step size to match the local shape of the density function. Implementation is arguably easier and more efficient than Gibbs sampling or simple Metropolis updates.
Note that, in contrast to many available methods for generating random numbers from non-uniform distributions, random variates generated directly by this approach will exhibit serial statistical dependence. In other words, not all points have the same independent likelihood of selection. This is because to draw the next sample, we define the slice based on the value of f(x) for the current sample. However, the generated samples are [[Markov property|markovian]], and are therefore expected to converge to the correct distribution in long run.
Slice Sampling requires that the distribution to be sampled be evaluable. One way to relax this requirement is to substitute an evaluable distribution which is proportional to the true unevaluable distribution.
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