This book is a rigorous but practical presentation of the Bayesian techniques of uncertainty quantification, with applications in R. This volume includes mathematical arguments at the level necessary to make the presentation rigorous and the assumptions clearly established, while maintaining a...

We build on Fackler and King (1990) and propose a 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. The model is a Bayesian dynamic beta Markov random field which allows for possible...

A method for treating Contingent Valuation data obtained from a polychotomous response format designed to accommodate respondent uncertainty is developed. The parameters that determine the probability of indefinite responses are estimated and used to truncate utility distributions within a...

We estimate a generalized version of the Long-Run Risk model in a panel of developed and developing countries using consumption, dividend growth, and asset returns data by utilizing the particle filter, while allowing for measurement errors in consumption data at quarterly and annual...

We introduce an additive stochastic mortality model which allows joint modelling and forecasting of underlying death causes. Parameter families for mortality trends can be chosen freely. As model settings become high dimensional, Markov chain Monte Carlo (MCMC) is used for parameter estimation....