In this paper we prove a deterministic approximation theorem for a sequence of Markov decision processeswith finitely many actions and general state spaces as they appear frequently in economics, game theory and operations research. Using viscosity solution methods no a-priori differentiabililty...

We consider the optimal dividend control problem to find an optimal strategy under the constraint that dividend rates are restricted such that the expected total discounted dividends is maximized for an insurance company. The evolution of the reserve is modeled by a diffusion process with drift...

We consider the optimal dividend distribution problem of a financial corporation whose surplus is modeled by a general diffusion process with both the drift and the diffusion coefficients depending on the external economic regime as well as the surplus itself through general functions. The aim...

This article introduces two sequential heuristics that are designed to overcome some of the practical limitations of the Entropy Pooling (EP) method. Both of these heuristics repeatedly apply the original EP method to sequentially arrive at the posterior probability and usually lead to...

Bayesian methods have become increasingly popular in the past two decades. With the constant rise of computational power even very complex models can be estimated on virtually any modern computer. Moreover, interest has shifted from conditional mean models to probabilistic distributional models...

We propose a new methodology for structural estimation of infinite horizon dynamic discrete choice models. We combine the Dynamic Programming (DP) solution algorithm with the Bayesian Markov Chain Monte Carlo algorithm into a single algorithm that solves the DP problem and estimates the...

This paper provides a step-by-step guide to estimating infinite horizon discrete choice dynamic programming (DDP) models using a new Bayesian estimation algorithm (Imai, Jain and Ching, Econometrica 77:1865-1899, 2009) (IJC). In the conventional nested fixed point algorithm, most of the...

Markov chain Monte Carlo (MCMC) methods have an important role in solving high dimensionality stochastic problems characterized by computational complexity. Given their critical importance, there is need for network and security risk management research to relate the MCMC quantitative...

An information-theoretic thought experiment is developed to clarify why the maximum entropy methodology is appropriate for predicting the equilibrium state of economic systems. As a first step, object allocation problems, modeled as knapsack problems, are shown to be equivalent to congestion...

Many products and services can be described as mixtures of ingredients whose proportions sum to one. Specialized models have been developed for linking the mixture proportions to outcome variables, such as preference, quality and liking. In many scenarios, only the mixture proportions matter for...