J. Birge - Academia.edu (original) (raw)

Papers by J. Birge

Research paper thumbnail of Introduction to Stochastic Dynamic Programming

Journal of the American Statistical Association, 1986

Research paper thumbnail of Improving the Numerical Performance of BLP Static and Dynamic Discrete Choice Random Coefficients Demand Estimation

Research paper thumbnail of Stochastic programming approaches to stochastic scheduling

Journal of Global Optimization, 1996

Research paper thumbnail of Assessing the effects of machine breakdowns in stochastic scheduling

Operations Research Letters, 1988

Research paper thumbnail of A Stochastic Programming Approach to the Airline Crew Scheduling Problem

Transportation Science, 2006

Research paper thumbnail of Finite buffer polling models with routing

European Journal of Operational Research, 2005

Research paper thumbnail of Optimal Commissions and Subscriptions in Networked Markets

Research paper thumbnail of Split Sampling: Expectations, Normalisation and Rare Events

Research paper thumbnail of Split Sampling: Expectations, Normalisation and Rare Events

In this paper we develop a methodology that we call split sampling methods to estimate high dimen... more In this paper we develop a methodology that we call split sampling methods to estimate high dimensional expectations and rare event probabilities. Split sampling uses an auxiliary variable MCMC simulation and expresses the expectation of interest as an integrated set of rare event probabilities. We derive our estimator from a Rao-Blackwellised estimate of a marginal auxiliary variable distribution. We illustrate our method with two applications. First, we compute a shortest network path rare event probability and compare our method to estimation to a cross entropy approach. Then, we compute a normalisation constant of a high dimensional mixture of Gaussians and compare our estimate to one based on nested sampling. We discuss the relationship between our method and other alternatives such as the product of conditional probability estimator and importance sampling. The methods developed here are available in the R package: SplitSampling.

Research paper thumbnail of Operational decisions, capital structure, and managerial compensation: A news vendor perspective

Research paper thumbnail of Designing approximation schemes for stochastic optimization problems, in particular for stochastic programs with recourse

Research paper thumbnail of The achievable region approach to the optimal control of stochastic systems

Journal of the Royal …, 1999

The achievable region approach seeks solutions to stochastic optimization problems by characteriz... more The achievable region approach seeks solutions to stochastic optimization problems by characterizing the space of all possible performances (the achievable region) of the system of interest and optimizing the overall system-wide performance objective over this space. ...

Research paper thumbnail of Exponential convergence of two-stage stochastic programming

Research paper thumbnail of Bounds on optimal values in stochastic scheduling

Operations Research Letters

Research paper thumbnail of A Stochastic Electricity Market Clearing Formulation with Consistent Pricing Properties

Operations Research, 2017

Research paper thumbnail of Local Discontinuous Galerkin Method for Portfolio Optimization with Transaction Costs

SSRN Electronic Journal, 2000

Research paper thumbnail of Inverse Optimization for the Recovery of Market Structure from Market Outcomes: An Application to the MISO Electricity Market

SSRN Electronic Journal, 2000

Research paper thumbnail of Adaptive Designs for Clinical Trials: Learning while Treating

Research paper thumbnail of On Some Dominance Results In Acheduling

Research paper thumbnail of Special Issue: Operational Research in Risk Management

Research paper thumbnail of Introduction to Stochastic Dynamic Programming

Journal of the American Statistical Association, 1986

Research paper thumbnail of Improving the Numerical Performance of BLP Static and Dynamic Discrete Choice Random Coefficients Demand Estimation

Research paper thumbnail of Stochastic programming approaches to stochastic scheduling

Journal of Global Optimization, 1996

Research paper thumbnail of Assessing the effects of machine breakdowns in stochastic scheduling

Operations Research Letters, 1988

Research paper thumbnail of A Stochastic Programming Approach to the Airline Crew Scheduling Problem

Transportation Science, 2006

Research paper thumbnail of Finite buffer polling models with routing

European Journal of Operational Research, 2005

Research paper thumbnail of Optimal Commissions and Subscriptions in Networked Markets

Research paper thumbnail of Split Sampling: Expectations, Normalisation and Rare Events

Research paper thumbnail of Split Sampling: Expectations, Normalisation and Rare Events

In this paper we develop a methodology that we call split sampling methods to estimate high dimen... more In this paper we develop a methodology that we call split sampling methods to estimate high dimensional expectations and rare event probabilities. Split sampling uses an auxiliary variable MCMC simulation and expresses the expectation of interest as an integrated set of rare event probabilities. We derive our estimator from a Rao-Blackwellised estimate of a marginal auxiliary variable distribution. We illustrate our method with two applications. First, we compute a shortest network path rare event probability and compare our method to estimation to a cross entropy approach. Then, we compute a normalisation constant of a high dimensional mixture of Gaussians and compare our estimate to one based on nested sampling. We discuss the relationship between our method and other alternatives such as the product of conditional probability estimator and importance sampling. The methods developed here are available in the R package: SplitSampling.

Research paper thumbnail of Operational decisions, capital structure, and managerial compensation: A news vendor perspective

Research paper thumbnail of Designing approximation schemes for stochastic optimization problems, in particular for stochastic programs with recourse

Research paper thumbnail of The achievable region approach to the optimal control of stochastic systems

Journal of the Royal …, 1999

The achievable region approach seeks solutions to stochastic optimization problems by characteriz... more The achievable region approach seeks solutions to stochastic optimization problems by characterizing the space of all possible performances (the achievable region) of the system of interest and optimizing the overall system-wide performance objective over this space. ...

Research paper thumbnail of Exponential convergence of two-stage stochastic programming

Research paper thumbnail of Bounds on optimal values in stochastic scheduling

Operations Research Letters

Research paper thumbnail of A Stochastic Electricity Market Clearing Formulation with Consistent Pricing Properties

Operations Research, 2017

Research paper thumbnail of Local Discontinuous Galerkin Method for Portfolio Optimization with Transaction Costs

SSRN Electronic Journal, 2000

Research paper thumbnail of Inverse Optimization for the Recovery of Market Structure from Market Outcomes: An Application to the MISO Electricity Market

SSRN Electronic Journal, 2000

Research paper thumbnail of Adaptive Designs for Clinical Trials: Learning while Treating

Research paper thumbnail of On Some Dominance Results In Acheduling

Research paper thumbnail of Special Issue: Operational Research in Risk Management