Probabilistic Measures and Integrals: How to Aggregate Imprecise Data

2020 ◽  
pp. 78-91
Author(s):  
Michał Boczek ◽  
Lenka Halčinová ◽  
Ondrej Hutník ◽  
Marek Kaluszka
Keyword(s):  
Imprecise Data ◽  
Probabilistic Measures

scholarly journals Communicative efficiency and differential case marking: a reverse-engineering approach

2021 ◽  
Vol 7 (s3) ◽  
Author(s):  
Natalia Levshina
Keyword(s):  
Reverse Engineering ◽  
Conditional Probabilities ◽  
Case Marking ◽  
Poisson Models ◽  
Linguistic Patterns ◽  
Cue Reliability ◽  
Communicative Efficiency ◽  
Cue Availability ◽  
Probabilistic Measures ◽  
Better Than

Abstract The use of differential case marking of A and P has been explained in terms of efficiency (economy) and markedness. The present study tests predictions based on these accounts, using conditional probabilities of a particular feature given the syntactic role (cue availability), and conditional probabilities of a particular syntactic role given the feature in question (cue reliability). Cue availability serves as a measure of markedness, whereas cue reliability is central for the efficiency account. Similar to reverse engineering, we determine which of the probabilistic measures could have been responsible for the recurrent cross-linguistic patterns described in the literature. The probabilities are estimated from spontaneous informal dialogues in English and Russian (Indo-European), Lao (Tai-Kadai), N||ng (Tuu) and Ruuli (Bantu). The analyses, which involve a series of mixed-effects Poisson models, clearly demonstrate that cue reliability matches the observed cross-linguistic patterns better than cue availability. Thus, the results support the efficiency account of differential marking.

Bootstrap approach to the multi-sample test of means with imprecise data

2006 ◽  
Vol 51 (1) ◽  
pp. 148-162 ◽  
Author(s):  
María Ángeles Gil ◽  
Manuel Montenegro ◽  
Gil González-Rodríguez ◽  
Ana Colubi ◽  
María Rosa Casals
Keyword(s):  
Imprecise Data ◽  
Bootstrap Approach ◽  
Sample Test

Combat modeling with imprecise data

1992 ◽  
Vol 7 (3) ◽  
pp. 277-291
Author(s):  
V. Protopopescu ◽  
R. Yager ◽  
J. Dockery
Keyword(s):  
Imprecise Data ◽  
Combat Modeling

scholarly journals Integration of different-quality data in short-term mining planning

2015 ◽  
Vol 68 (2) ◽  
pp. 221-227 ◽  
Author(s):  
Cristina Paixão Araújo ◽  
João Felipe Coimbra Leite Costa
Keyword(s):  
Mineral Exploration ◽  
Quality Data ◽  
Imprecise Data ◽  
Short Term ◽  
Mining Operations ◽  
Different Types ◽  
Block Models ◽  
The Impact ◽  
Block Distribution ◽  
Ordinary Cokriging

AbstractDecisions, from mineral exploration to mining operations, are based on grade block models obtained from samples. This study evaluates the impact of using imprecise data in short-term planning. The exhaustive Walker Lake dataset is used and is considered as the source for obtaining the true grades. Initially, samples are obtained from the exhaustive dataset at regularly spaced grids of 20 × 20 m and 5 × 5 m. A relative error (imprecision) of ±25% and a 10% bias are added to the data spaced at 5 × 5 m (short-term geological data) in different scenarios. To combine these different types of data, two methodologies are investigated: cokriging and ordinary kriging. Both types of data are used to estimate blocks with the two methodologies. The grade tonnage curves and swath plots are used to compare the results against the true block grade distribution. In addition, the block misclassification is evaluated. The results show that standardized ordinary cokriging is a better methodology for imprecise and biased data and produces estimates closer to the true grade block distribution, reducing block misclassification.

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