This post is about change of variables in Markov chain Monte Carlo (MCMC), which is used quite often when the target distribution is supported on a subset of . For example, the Exponential distribution and the Log-Normal distribution are only supported on positive reals.

Consider a target distribution that is supported on a subset . If we use a random walk proposal , then we might end up with a proposal such that and, this might cause too few acceptance in the MCMC chain. If we can find a transformation that is one-to-one, differentiable and spans , then we can consider a proposal where . This proposal always yields a proposal such that

Of course, when we employ such a transformation in the proposal kernel, we need to be careful about evaluating the proposal densities. We know that the acceptance probability is , and it should be no surprise that unless is the identity map.

Let’s work out the acceptance ratio together carefully. Recall that change of variables proceeds as follows: when and we consider the transformation , the pdf of is

When we apply this to the kernels and we get that

**Example 1** *{Symmetric proposal on transformed space} If is a symmetric proposal, then the acceptance probability becomes *

Here are two common transformations.

**Example 2 (Log-transformation for supported on )**

If , then and acceptance probability is

**Example 3 (Logit transformation for supported on )** * If ,then the inverse transformation is The acceptance probability is *

This post is the first one in the category `trivial matters’, where I formally write down some notes to myself about tricks and facts that I repeatedly use but (unfortunately) need to re-derive everytime I use them.

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