### Table 5. Bayesian Networks for the Scales of the ACALQ Instrument

2004

"... In PAGE 6: ..., 2001). Probabilistic dependencies between all of the four scales are presented in Table5 . Bayesian search algorithm evaluated the data sets in order to find the model with the highest probability.... ..."

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### Table 5. Bayesian Networks for the Scales of the ACALQ Instrument

2004

"... In PAGE 6: ..., 2001). Probabilistic dependencies between all of the four scales are presented in Table5 . Bayesian search algorithm evaluated the data sets in order to find the model with the highest probability.... ..."

Cited by 1

### Table 2: Comparison of observed possibility disjunctions with both possibilistic and probabilistic predictions *Guarantee that the difference between the model and the data is negligible. ** quot;undetermined effect quot;: negligibility cannot be claimed Conjunction. In the independent condition, the data fit was better with the possibilistic model than with the probabilistic one (see Table 3) because the difference between the data and the latter model was significant. In addition, Bayesian inference allowed us to claim negligibility of the difference between data and the possibilistic model, but not between data and the probabilistic model. Surprisingly, the best fit appeared with possibility measures (r(46) = .928; mean difference lt; 0.4%). This could be due to the fact that in the part of the scale where most conjunction ratings were (below 50), possibility measures are more relevant than necessity measures.

### Table 2. Computational models of language using probabilistic and statistical methodsa

"... In PAGE 2: ...earning; what is distinctive is the specific structures (e.g. trees, dependency diagrams) relevant for language. In computational linguistics, the practical challenge of parsing and interpreting corpora of real language (typi- cally text, sometimes speech) has led to a strong focus on probabilistic methods ( Table2 ). However, computational linguistics often parts company from standard linguistic www.... ..."

### Table 2. Computational models of language using probabilistic and statistical methodsa

"... In PAGE 2: ...earning; what is distinctive is the specific structures (e.g. trees, dependency diagrams) relevant for language. In computational linguistics, the practical challenge of parsing and interpreting corpora of real language (typi- cally text, sometimes speech) has led to a strong focus on probabilistic methods ( Table2 ). However, computational linguistics often parts company from standard linguistic www.... ..."

### Table 1: A comparison between the Bayesian One-Shot learning algorithm and alternative approaches to object category recogni- tion. The error rate quoted for the Bayesian One-Shot model is for BH training images.

2003

"... In PAGE 6: ... In this case it achieves a recognition rate as high as BKBEB1, given only 1 training ex- ample. Table1 compares our algorithm with some pub- lished object categorization methods. Note that our algo- rithm has significantly faster learning speed due to much... In PAGE 8: ... Conclusions and future work We have demonstrated that given a single example (or just a few), we can learn a new object category. As Table1 shows, this is beyond the capability of existing algorithms. In order to explore this idea we have developed a Bayesian learn- ing framework based on representing object categories with probabilistic models.... ..."

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### Table 3 lists the comparison between various range free schemes. Tian He et al. experiment with several parameters such as node density (ND), anchors heard (AH), anchor to node range ratio (ANR), anchor percentage (AP), degree of irregularity (DOI), GPS error and placement of node and anchors. Recently research on localization is focused on incorporating the mobility model. Although mobility makes the analysis more difficult, more accuracy is obtained. In [15], Lingxuan Hu etal. use a sequential Monte Carlo Localization method and argues that they exploited mobility to improve accuracy and preci- sion of localization. Probabilistic techniques, such as Markov modeling, Kalman filtering and Bayesian analysis can also be used to determine the absolute loca- tion of a mobile node [18].

2005

"... In PAGE 10: ... Table3 . Comparison of range free schemes Table 4 gives a global view of localization techniques classified by achievable accuracy and the type of location estimation used for various technologies.... ..."

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### Table 2: Generalization accuracies and bit rates for fully Bayesian method

2003

"... In PAGE 5: ...airings of the study reported in section 2.1. We compare the generalization accuracies of the fully probabilistic model (full Bayes) with those of a classi- cal approach that does feature extraction separately. Table2 also shows in brackets the probabilities of the null hypothesis that the result of one method are equal to the method in the previous column. We may thus conclude that a fully Bayesian approach significantly outperforms classifications obtained when conditioning on feature estimates.... ..."

### Table 7. Bayesian Network Model and the Importance Ranking of the 28 Item Solution Measuring Self-evaluated Motivation

"... In PAGE 12: ..., 2001). Probabilistic dependencies between all of the motivational scale variables are presented in Table7... ..."

### Table 1. Probabilistic User Model

2006

"... In PAGE 4: ... In these studies, users marked 85% of formula cells on average when testing and debugging spreadsheets, often placing check-marks on cells, and rarely placing a23-marks on cells. Of the cells that users marked, users in our earlier studies made mistakes according to the probabilities given in Table1 , so for our study, we simulated user behavior based on these probabilities. The bold numbers in Table 1 highlight false positive (check on incorrect value) and false negative (a23 on correct value) oracle mistakes.... ..."

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