We propose a method for sequential Bayesian kernel regression. As is the case for the popular Relevance Vector Machine (RVM) [10, 11], the method automatically identifies the number and locations of the kernels. Our algorithm overcomes some of the computational difficulties related to batch methods for kernel regression. It is non-iterative, and requires only a single pass over the data. It is ...

Bayesian estimation of DSGE models typically uses Markov chain Monte Carlo as importance sampling (IS) algorithms have a difficult time in high-dimensional spaces. I develop improved IS algorithms for DSGE models using recent advances in Monte Carlo methods known as sequential Monte Carlo samplers. Sequential Monte Carlo samplers are a generalization of particle filtering designed for full simu...

We present the Sequential Ensemble Transform (SET) method, an approach for generating approximate samples from a Bayesian posterior distribution. The method explores distribution by solving sequence of discrete optimal transport problems to produce series plans which map prior samples. prove that Dirac mixture distributions produced SET converges weakly true as sample size approaches infinity. ...