For investigating the individual-environment interplay and individual differences in response to environmental exposures as captured by models of Environmental Sensitivity including Diathesis-stress, Differential Susceptibility and Vantage Sensitivity, over the last few years a series of statistical guidelines have been proposed. However, available solutions suffer of computational problems especially relevant when sample size is not sufficiently large, a common condition in observational and clinical studies. In the current contribution, we propose a Bayesian solution for estimating interaction parameters via Monte Carlo Markov Chains (MCMC), adapting Widaman et al. (2012) Non-linear Least Squares (NLS) approach. Findings from an applied exemplification and a simulation study showed that with relatively big samples both MCMC and NLS estimates converged on the same results. Conversely, MCMC clearly outperformed NLS, resolving estimation problems and providing more accurate estimates, particularly with small samples and greater residual variance. Implications for applied knowledge and evidence-based practice are discussed.