Monte Carlo methods for Bayesian palaeoclimate reconstruction

In palaeoclimate reconstruction, the natural modelling direction is forwards from climate to sensors to proxy measurements. Statistical methods can be used to invert this direction, making climate inferences from proxy measurements. Among these methods, the Bayesian method would seem to deal best with the substantial epistemic uncertainties about climate, and about its impact on sensors. The main challenge is to perform this inference efficiently within a simulation approach. This paper reviews the Importance Sampling approach to Bayesian palaeoclimate reconstruction, and then goes on to demonstrate the value of recent advances in Markov chain Monte Carlo (MCMC) inference