✦ LIBER ✦

Estimation of probabilities subject to linear inequality constraints

✍ Scribed by H Irtel

Publisher: Elsevier Science
Year: 1980
Tongue: English
Weight: 199 KB
Volume: 21
Category: Article
ISSN: 0022-2496
DOI: 10.1016/0022-2496(80)90007-3

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

Estimating economic relationships subjec

Estimating economic relationships subject to firm- and time-varying equality and inequality constraints

✍ Dr. Christopher J. O'Donnell; Alicia N. Rambaldi; Howard E. Doran 📂 Article 📅 2001 🏛 John Wiley and Sons 🌐 English ⚖ 160 KB

## Abstract Applied econometricians often fail to impose economic regularity constraints in the exact form economic theory prescribes. We show how the Singular Value Decomposition (SVD) Theorem and Markov Chain Monte Carlo (MCMC) methods can be used to rigorously impose time‐ and firm‐varying equal

On the Estimation of Intra-Sire Regressi

On the Estimation of Intra-Sire Regression Heritability Subject to Linear Constraint

✍ Dr. V. T. Prabhakaran; Seema Jaggi 📂 Article 📅 1996 🏛 John Wiley and Sons 🌐 English ⚖ 239 KB 👁 2 views

On estimating inter-subject variability

On estimating inter-subject variability of choice probabilities under observability constraints

✍ Robert Bartoszyński; Madan L Puri 📂 Article 📅 1982 🏛 Elsevier Science 🌐 English ⚖ 456 KB

Uniform reciprocal approximation subject

Uniform reciprocal approximation subject to linear constraints

✍ B.L Chalmers; E.H Kaufman Jr.; D.J Leeming; G.D Taylor 📂 Article 📅 1984 🏛 Elsevier Science 🌐 English ⚖ 920 KB

Maximizing bilinear forms subject to lin

Maximizing bilinear forms subject to linear constraints

✍ Steven J. Leon 📂 Article 📅 1994 🏛 Elsevier Science 🌐 English ⚖ 473 KB

Flexible solutions to linear programs un

Flexible solutions to linear programs under uncertainty: Inequality constraints

✍ Y. Friedman; G. V. Reklaitis 📂 Article 📅 1975 🏛 American Institute of Chemical Engineers 🌐 English ⚖ 700 KB

## Abstract In most industrial applications the linear model used for optimization by linear programming involves significant uncertainties and inaccuracies in the model parameters. This paper presents a framework which allows uncertainties in the matrix elements of the linear program to be taken i