As silicon manufacturing technology reaches the nanoscale, architectural designs need to accommodate the uncertainty inherent at such scales. These uncertainties are germane in the miniscule dimension of the devices, quantum physical effects, reduced noise margins, system energy levels reaching computing thermal limits, manufacturing defects, aging and many other factors. Defect tolerant architectures and their reliability measures will gain importance for logic and micro-architecture designs based on nano-scale substrates. Recently, Markov Random Field (MRF) has been proposed as a model of computation for nanoscale logic gates. In this paper, we take this approach further by automating this computational scheme and a Belief Propagation algorithm. We have developed MATLAB based libraries and toolset for fundamental logic gates that can compute output probability distributions and entropies for speci.ed input distributions. Our tool eases evaluation of reliability measures of combinational logic blocks. The effectiveness of this automation is illustrated in this paper by automatically deriving various reliability results for defect-tolerant architectures, such as Triple Modular Redundancy (TMR), Cascaded Triple Modular Redundancy (CTMR) and multi-stage iterations of these. These results are used to analyze tradeoffs between reliability and redundancy for these architectural configurations.