We explore the effects of normalizing the proposal density in Markov Chain Monte Carlo algorithms in the context of reconstructing the conductivity term $K$ in the $2$-dimensional heat equation, given temperatures at the boundary points, $d$. We approach this nonlinear inverse problem by implementing a Metropolis-Hastings Markov Chain Monte Carlo algorithm. Markov Chains produce a probability distribution of possible solutions conditional on the observed data. We generate a candidate solution $K'$ and solve the forward problem, obtaining $d'$. At step $n$, with some probability $\alpha$, we set $K_{ n+1}=K'$. We identify certain issues with this construction, stemming from large and fluctuating values of our data terms. Using this framework, we develop normalization terms $z_0,z$ and parameters $\lambda$ that preserve the inherently sparse information at our disposal. We examine the results of this variant of the MCMC algorithm on the reconstructions of several $2$-dimensional conductivity functions.
Categories: math.NA