We propose an alternate parameterization of stationary regular finite-state Markov chains, and a decomposition of the parameter into time reversible and time irreversible parts. We demonstrate some useful properties of the decomposition, and propose an index for a certain type of time...

I introduce the HESSIAN method for semi-Gaussian state space models with univariate states. The vector of states a=(a^1, ... , a^n) is Gaussian and the observed vector y= (y^1 , ... , y^n ) need not be. I describe a close approximation g(a) to the density f(a|y). It is easy and fast to evaluate...

We complete the development of a testing ground for axioms of discrete stochastic choice. Our contribution here is to develop new posterior simulation methods for Bayesian inference, suitable for a class of prior distributions introduced by McCausland and Marley (2013). These prior distributions...