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...

McCausland (2004a) describes a new theory of random consumer demand. Theoretically consistent random demand can be represented by a "regular" "L-utility" function on the consumption set X. The present paper is about Bayesian inference for regular L-utility functions. We express prior and...

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...

This paper presents a new theory of random consumer demand. The primitive is a collection of probability distributions, rather than a binary preference. Various assumptions constrain these distributions, including analogues of common assumptions about preferences such as transitivity,...

I introduce the HESSIAN (highly efficient simulation smoothing in a nutshell) method for numerically efficient simulation smoothing in state space models with univariate states. Given a vector θ of parameters, the vector of states α=(α1,…,αn) is Gaussian and the observed vector...

I present a theory of random consumer demand. The primitive is a collection of probability distributions on budgets. Axioms constrain these distributions, including analogues of preference axioms, such as transitivity, monotonicity and convexity. Results establish a complete representation of...

The BACC software provides its users with tools for Bayesian Analysis, Computation and Communications. The current version of the software, described here, implements these tools as extensions to popular mathematical applications such as Matlab, S-PLUS, R, and Gauss, running under Windows, Linux...