Use MCMC for model fit uncertainties

[cplee1]

The uncertainties on the model parameters are calculated from the covariance matrix, which assumes that the likelihood is symmetric (i.e. normally distributed) around the best-fit value. This method can lead to underestimated and inaccurate uncertainties.

A more robust method would be to use a Markov Chain Monte Carlo (MCMC) algorithm to sample the probability distributions of the model parameters. This would also be simpler than implementing an equivalent frequentist method.