Given Keynes's Definitions of Logical Probability and Evidential Weight, It Is Impossible for Keynes’s Approach to Measurement to Be an Ordinal Theory; His 'Non Numerical' Probabilities Must Be Based on Inexact and Imprecise Measurement Using Approximation Involving Boolean Upper and Lower Probability Bounds and Bounded Outcomes

2019 ◽  
Author(s):  
Michael Emmett Brady
Keyword(s):  
Lower Probability ◽  
Logical Probability ◽  
Probability Bounds ◽  
Upper And Lower Probability

Upper and Lower Probability

1994 ◽  
pp. 109-133 ◽  
Author(s):  
Terrence L. Fine
Keyword(s):  
Lower Probability ◽  
Upper And Lower Probability

scholarly journals Towards a Frequentist Theory of Upper and Lower Probability

1982 ◽  
Vol 10 (3) ◽  
pp. 741-761 ◽  
Author(s):  
Peter Walley ◽  
Terrence L. Fine
Keyword(s):  
Lower Probability ◽  
Frequentist Theory ◽  
Upper And Lower Probability

Upper and Lower Probability Inferences Based on a Sample from a Finite Univariate Population

Biometrika ◽  
1967 ◽  
Vol 54 (3/4) ◽  
pp. 515 ◽  
Author(s):  
A. P. Dempster
Keyword(s):  
Lower Probability ◽  
Upper And Lower Probability

Upper and lower probability inferences based on a sample from a finite univariate population

Biometrika ◽  
1967 ◽  
Vol 54 (3-4) ◽  
pp. 515-528 ◽  
Author(s):  
A. P. DEMPSTER
Keyword(s):  
Lower Probability ◽  
Upper And Lower Probability

REWARD VERSUS RISK IN UNCERTAIN INFERENCE: THEOREMS AND SIMULATIONS

2012 ◽  
Vol 5 (4) ◽  
pp. 574-612 ◽  
Author(s):  
GERHARD SCHURZ ◽  
PAUL D. THORN
Keyword(s):  
Computer Simulations ◽  
Lower Probability ◽  
Conditional Probabilities ◽  
Uncertain Inference ◽  
Probability Bounds ◽  
Versus Risk

AbstractSystems oflogico-probabilistic(LP) reasoning characterize inference from conditional assertions that express high conditional probabilities. In this paper we investigate four prominent LP systems, the systemsO, P,Z, andQC. These systems differ in the number of inferences they licence (O⊂ P ⊂Z⊂QC). LP systems that license more inferences enjoy the possiblerewardof deriving more true and informative conclusions, but with this possible reward comes theriskof drawing more false or uninformative conclusions. In the first part of the paper, we present the four systems and extend each of them by theorems that allow one to compute almost-tight lower-probability-bounds for the conclusion of an inference, given lower-probability-bounds for its premises. In the second part of the paper, we investigate by means of computer simulations which of the four systems provides the best balance ofrewardversusrisk. Our results suggest that systemZoffers the best balance.

Sign in / Sign up

Export Citation Format

Share Document