A Bayesian Beta Markov Random Field Calibration of the Term Structure of   Implied Risk Neutral Densities
  We build on the work in Fackler and King 1990, and propose a more general calibration model for implied risk neutral densities. Our model allows for the joint calibration of a set of densities at different maturities and dates through a Bayesian dynamic Beta Markov Random Field. Our approach allows for possible time dependence between densities with the same maturity, and for dependence across maturities at the same point in time. This approach to the problem encompasses model flexibility, parameter parsimony and, more importantly, information pooling across densities. 
Roberto Casarin and Fabrizio Leisen and German Molina and Enrique ter   Horst
stat.AP q-fin.CP