Bayesian Linear Programming
Bayesian Linear Programming (BLP) integrates Bayesian statistical inference with classical linear programming to handle uncertainty in model parameters such as objective function coefficients, constraint coefficients, or right-hand-side values. Instead of treating parameters as fixed or governed by worst-case bounds, BLP uses prior beliefs updated by data to form posterior distributions, which then guide the LP formulation and solution, producing decisions that are optimal in a probabilistic, data-informed sense.
Kilderegistrering
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- Dantzig, G. B. (1963). Linear Programming and Extensions. Princeton University Press, Princeton, NJ. · ISBN 9780691059136
- Zellner, A. (1971). An Introduction to Bayesian Inference in Econometrics. Wiley, New York. · ISBN 9780471169376
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