I thought I had great intuition and mathematical understanding of the Metropolis-Hastings algorithm, until closer inspection... as I started compiling my notes, I realized I do not understand the rejection step of the algorithm.
Here is what I understood:
We have a target distribution $\pi(x)$, and we construct a transition kernel $K(x' \mid x)$ such that the detailed balance equation holds. $$\pi(x)K(x' \mid x) = \pi(x') K(x \mid x')$$
We can choose $$K(x, x') = \displaystyle \alpha(x, x')q(x \mid x')$$ Where $\alpha$ is the Metropolis-Hastings ratio, and $q$ is some proposal distribution. This particular construction of $\alpha$ helps correct the discrepancies in our detailed balance equation, thus providing us flexibility in choosing $q$.
Where I am having problems:
- How do I think about $K$ as a distribution (or even visualize)? In particular, what is $\alpha(x, x')$?
- What's going on with the sampling step where we reject and stay at $x$? Originally I thought of it as some correction function, but the rejection meant $X' := X$ and thus instinctively, I want think of $K(X, X')$ as a mixture of a $\delta_{\{X\}}(X')$ and $q(X'|X)$, however, the mass associated with this dirac delta varies depending on $x'$...? Not quite a mixture model.
Should I be looking to interpret $\alpha$ as some form of accept-reject algorithm?- How do I write $K$ as a density?
Edit: Maybe this should be a question not a comment:
With regards to the order of derivations (ie. motivation), is the following a reasonable thought process?
- We want to construct some transition kernel invariant to our target distribution.
- We select some proposal distribution, and notice it breaks detailed balance equation.
- we correct it with an acceptance-probability.
- Due to this correction probability, we need to have some action corresponding to the complement accepting the proposed state -> we remain at our current state.
Question: Is this choice of "remain at current state" arbitrary?