Alternating current (AC) voltammetric techniques are experimentally powerful as they enable Faradaic current to be isolated from non-Faradaic contributions. Finding the best global fit between experimental voltammetric data and simulations based on reaction models requires searching a substantial parameter space at high resolution. In this paper, we estimate parameters from purely sinusoidal voltammetry (PSV) experiments, investigating the redox reactions of a surface-confined ferrocene derivative. The advantage of PSV is that a complete experiment can be simulated relatively rapidly, compared to other AC voltammetric techniques. In one example involving thermodynamic dispersion, a PSV parameter inference effort requiring 7,500,000 simulations was completed in 7 h, whereas the same process for our previously used technique, ramped Fourier transform AC voltammetry (ramped FTACV), would have taken 4 days. Using both synthetic and experimental data with a surface confined diazonium substituted ferrocene derivative, it is shown that the PSV technique can be used to recover the key chemical and physical parameters. By applying techniques from Bayesian inference and Markov chain Monte Carlo methods, the confidence, distribution, and degree of correlation of the recovered parameters was visualized and quantified.