Option pricing using mixed normal heteroscedasticity models is considered. It is explained how to perform inference and price options in a Bayesian framework. The approach allows to easily compute risk neutral predictive price densities which take into account parameter uncertainty. In an application to the S&P 500 index, classical and Bayesian inference is performed on the mixture model using the available return data. Comparing the ML estimates and posterior moments small differences are found. When pricing a rich sample of options on the index, both methods yield similar pricing errors measured in dollar and implied standard deviation losses, and it turns out that the impact of parameter uncertainty is minor. Therefore, when it comes to option pricing where large amounts of data are available, the choice of the inference method is unimportant. The results are robust to different specifications of the variance dynamics but show however that there might be scope for using Bayesian methods when considerably less data is available for inference.