MCMC with progressive demollification of delta distributions Edit: I simplified the example to a canonical case for clarity.
Given an integral $\int_{\Omega}{g(\mathbf{x})}$ with a well-posed integrand $g(\mathbf{x})$ defined on some multidimensional space $\Omega$, one can integrate it successfully with a Markov Chain Monte Carlo method, in particular using Metropolis-Hastings method. Detailed balance and ergodicity are achieved with any well-posed (samplable) integrand. Transition kernel $K(\dot{},\dot{})$ is Harris recurrent and its transition probability is known for every pair $(\mathbf{x},\mathbf{y})$. The value of the desired integral is always finite, even in presence of delta distributions in the integrand. This is given.
In the case of interest, the integrand is of a form $f(\mathbf{x})=\delta_{\mathbf{x}_0}(\mathbf{x})\dot{} g(\mathbf{x}) + h(\mathbf{x})$ and consists of a delta distribution at some unknown location $\mathbf{x}_0\in \Omega$ and some regular (well-posed) non-zero functions $g(\mathbf{x})$ and $h(\mathbf{x})$. This delta distribution cannot be sampled explicitly or using numerical optimization (given). This makes such an integrand unsamplable with random walk. 
I mollify (approximate to the identity) this delta distribution using some mollifier (normalized smooth function $\phi_\epsilon(\mathbf{x})=\epsilon^{-1}\phi\left(\frac{\mathbf{x}}{\epsilon}\right)$ with some bandwidth $\epsilon$). This leads to a tempered integrand $f_\epsilon(\mathbf{x})=\phi_\epsilon(\mathbf{x}-\mathbf{x}_0)\dot{} g(\mathbf{x})+h(\mathbf{x})$. During the integration, at every step $n$, I gradually shrink the parameter $\epsilon_n$ to zero in order to achieve $f_{\epsilon_n} \to f$ as $n \to \infty$ in spirit of serial tempering and simulated annealing. 
One cannot use parallel or serial tempering here, as the probability of the proposal to descend from a tempered mixture to the original integrand $f$ at the exact location $\mathbf{x}_0$ of the delta distribution is zero.
Thus I have two rather similar questions: 


*

*Would the integral converge to the proper value $g(\mathbf{x_0})+\int_{\Omega}{h(\mathbf{x})}$ with such scheme? 

*What are the conditions for the asymptotic decrease rate of the sequence $\epsilon_n$ that guarantees the consistency of the MCMC integration? In other words, the rate that allows the integral to converge before parts of the integrand $f_{\epsilon_n}$ become unsamplable?

 A: I don't know how committed you are to decreasing epsilon at every step; doing so makes theory a bit harder, at least for me. In any case, as your second question suggests, the answer is that everything should work out fine if you're a little bit careful; checking that you're being careful depends on the values of g,h  and your space.
If you're willing to decrease epsilon by a factor of two at certain steps T(1), T(2), ... then the answers become:
1) Yes, if T(i) grows sufficiently quickly
2) There are many answers, depending on what you know! Bremaud's undergraduate text on Markov chains and such gives a proof that simulated annealing works which should be almost exactly what you want here. Essentially, within each epoch $T(i+1) - T(i)$, you should be able to guarantee mixing of your Markov chain to the extent that your integral is within some error $\epsilon(i)$ of the integral of $f_{\epsilon(i)}$ with probability at least e.g. $1 - i^{-2}$. Furthermore, you should be able to guarantee that the integral of $f_{\epsilon(i)}$ is within $\delta(i)$ of the integral of $f$. 
As long as $\epsilon(i)$ and $\delta(i)$ go to 0, and $T(i+1) - T(i)$ is sufficiently large for these conditions to hold, you will have convergence.
Of course, in real life, this requires knowledge of e.g. the spikiness of $f_{\epsilon}$ as well as good information about the convergence of your underlying chain. 
