In the book Introducing Monte Carlo Methods by Casella and Robert, there's a sentence with which I'm having some trouble to understand.

«If the domain explored in $q$ [proposal] is too small, compared with the range of $f$(the target's distribution density), the Markov Chain will have difficulties in exploring this range and will converge very slowly»

The acceptance probability depends on the proposed state $y$, through $$\frac{f(y)}{q(y|x)}$$ The higher this proportion, the higher the chances of accepting the proposed move.

Unintuitive to me at least, a very low transition prob. $q(y|x)$ w.r.t. $f(y)$ would increase the chances of accepting the new move. So, if I'm at $x$ and I'm proposed a move to a lower transition probability state, then I'll want to move...

Is that to make me explore the whole domain of $q$? How can the support of proposal distribution impact convergence of RH-MH algorithm?

  • $\begingroup$ You want to explore $f$, not $q$. And yeah, if $q(y|x)$ is small and $f(y)$ big you gotta “seize the opportunity” and jump before you miss your chance. $\endgroup$ – Taylor Aug 6 '18 at 15:47

While obviously biased, I have trouble to see the difficulty with that sentence! If the proposal distribution has a bounded support, the Markov chain can only move to a point within that support and nowhere else. This means connecting two values $y_1$ and $y_2$ that have positive densities, $f(y_1)>0$ and $f(y_2)>0$ but are far away will require many iterations of the Metropolis-Hastings algorithm. In the worst case, if the support of $f$ is made of two disjoint sets $A_1$ and $A_2$ with $y_1\in A_1$ and $y_2\in A_2$ and if the distance between both sets is larger than the diameter of the support of $q(\cdot|\cdot)$ the Markov chain cannot leave the $A_i$ it started from.

Here is an illustration on a basic example:



  if (runif(1)*target(x)*propal(x,prop)>target(prop)*propal(prop,x))

for (t in 2:T)

for (t in 2:T)


with outcome

enter image description here

This poor behaviour of $q$'s with small support is unrelated with the point that when $f(y)/q(y|x)$ is large the chain should move there, which is correct.

  • 1
    $\begingroup$ Now I get it. My confusion came from the (mis?)use of the words domain/range in the sentence. I would write something like «If the space explored by $q$ ( $\text{supp}(f)$ ) is too small when compared with the domain of $f$, the Markov Chain will have difficulties in exploring $f$'s domain and thus will converge very slowly, if at all for practical purposes. $\endgroup$ – An old man in the sea. Aug 7 '18 at 8:09
  • $\begingroup$ I hope you didn't take it badly, I'm not a native, and I'm self-studying this. So, I must absorb the most I can from each specific word. Since I have no one to ask in case of doubt, and sometimes using this forum helps, but it's not a perfect substitute for a physical professor. Also, I'm a really slow learner... ;) $\endgroup$ – An old man in the sea. Aug 7 '18 at 21:48

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