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Quantifying rates of charge carrier recombination is a crucial step in developing solar cells with high power conversion efficiencies. However, recovering characteristic parameters such as carrier mobility, doping concentration, and recombination rate constants is hindered by the interplay of the carrier dynamics with multiple recombination mechanisms. Interpretation of optoelectronic measurements, such as time-resolved photoluminescence (TRPL), usually relies on analytically tractable simplifications to the underlying physics models for carrier mobility and recombination, which sacrifices some of the information content of the measurement. We have recently shown that, by incorporating simulations of the complete carrier physics into a Bayesian analysis, previously unrecoverable material parameters, such as carrier mobility and the doping level, can be determined from TRPL measurements. Unfortunately, the large number of simulations required by a random sampling of the parameter space necessitated access to high-performance computing resources, limiting the usefulness of the approach. Here, we introduce an importance sampling algorithm (Metropolis Monte Carlo), which reduces the computational requirements by 2–3 orders of magnitude, rendering the Bayesian inference tractable on desktop computers. These developments affirm the utility of a simulation-driven analysis of optical characterization measurements, and make a physics-informed Bayesian inference available to all semiconductor researchers.