scholarly journals Transitivity correlation: A descriptive measure of network transitivity

2019 ◽  
Vol 7 (3) ◽  
pp. 353-375
Author(s):  
David Dekker ◽  
David Krackhardt ◽  
Tom A. B. Snijders

AbstractThis paper proposes that common measures for network transitivity, based on the enumeration of transitive triples, do not reflect the theoretical statements about transitivity they aim to describe. These statements are often formulated as comparative conditional probabilities, but these are not directly reflected by simple functions of enumerations. We think that a better approach is obtained by considering the probability of a tie between two randomly drawn nodes, conditional on selected features of the network. Two measures of transitivity based on correlation coefficients between the existence of a tie and the existence, or the number, of two-paths between the nodes are developed, and called “Transitivity Phi” and “Transitivity Correlation.” Some desirable properties for these measures are studied and compared to existing clustering coefficients, in both random (Erdös–Renyi) and in stylized networks (windmills). Furthermore, it is shown that in a directed graph, under the condition of zero Transitivity Correlation, the total number of transitive triples is determined by four underlying features: size, density, reciprocity, and the covariance between in- and outdegrees. Also, it is demonstrated that plotting conditional probability of ties, given the number of two-paths, provides valuable insights into empirical regularities and irregularities of transitivity patterns.

Author(s):  
E. D. Avedyan ◽  
Le Thi Trang Linh

The article presents the analytical results of the decision-making by the majority voting algorithm (MVA). Particular attention is paid to the case of an even number of experts. The conditional probabilities of the MVA for two hypotheses are given for an even number of experts and their properties are investigated depending on the conditional probability of decision-making by independent experts of equal qualifications and on their number. An approach to calculating the probabilities of the correct solution of the MVA with unequal values of the conditional probabilities of accepting hypotheses of each statistically mutually independent expert is proposed. The findings are illustrated by numerical and graphical calculations.


2016 ◽  
Vol 10 (2) ◽  
pp. 284-300 ◽  
Author(s):  
MARK J. SCHERVISH ◽  
TEDDY SEIDENFELD ◽  
JOSEPH B. KADANE

AbstractLet κ be an uncountable cardinal. Using the theory of conditional probability associated with de Finetti (1974) and Dubins (1975), subject to several structural assumptions for creating sufficiently many measurable sets, and assuming that κ is not a weakly inaccessible cardinal, we show that each probability that is not κ-additive has conditional probabilities that fail to be conglomerable in a partition of cardinality no greater than κ. This generalizes a result of Schervish, Seidenfeld, & Kadane (1984), which established that each finite but not countably additive probability has conditional probabilities that fail to be conglomerable in some countable partition.


Author(s):  
Kenny Easwaran

Conditional probability has been put to many uses in philosophy, and several proposals have been made regarding its relation to unconditional probability, especially in cases involving infinitely many alternatives that may have probability 0. This chapter briefly summarizes some of the literature connecting conditional probabilities to probabilities of conditionals and to Humphreys' Paradox for chances, and then investigates in greater depth the issues around probability 0. Approaches due to Popper, Rényi, and Kolmogorov are considered. Some of the limitations and alternative formulations of each are discussed, in particular the issues arising around the property of “conglomerability” and the idea that conditional probabilities may depend on a conditioning algebra rather than just an event.


2019 ◽  
Vol 29 (7) ◽  
pp. 938-971 ◽  
Author(s):  
Kenta Cho ◽  
Bart Jacobs

AbstractThe notions of disintegration and Bayesian inversion are fundamental in conditional probability theory. They produce channels, as conditional probabilities, from a joint state, or from an already given channel (in opposite direction). These notions exist in the literature, in concrete situations, but are presented here in abstract graphical formulations. The resulting abstract descriptions are used for proving basic results in conditional probability theory. The existence of disintegration and Bayesian inversion is discussed for discrete probability, and also for measure-theoretic probability – via standard Borel spaces and via likelihoods. Finally, the usefulness of disintegration and Bayesian inversion is illustrated in several examples.


1964 ◽  
Vol 14 (3) ◽  
pp. 741-742 ◽  
Author(s):  
Allen Jack Edwards ◽  
Paula Parks

Using 149 undergraduates, normative data were gathered on two measures of flexibility in the thought process, Guilford's Association IV and Object Naming. The distributions of scores were positively displaced, with differences between means being small and statistically non-significant. Product-moment correlation coefficients were statistically significant ( p = .01) for the total group, for men, and for women. No sex differences were found.


2014 ◽  
Vol 9 (3) ◽  
pp. 437-472 ◽  
Author(s):  
Cyrus Shaoul ◽  
R. Harald Baayen ◽  
Chris F. Westbury

What knowledge influences our choice of words when we write or speak? Predicting which word a person will produce next is not easy, even when the linguistic context is known. One task that has been used to assess context dependent word choice is the fill-in-the-blank task, also called the cloze task. The cloze probability of specific context is an empirical measure found by asking many people to fill in the blank. In this paper we harness the power of large corpora to look at the influence of corpus-derived probabilistic information from a word’s micro-context on word choice. We asked young adults to complete short phrases called n-grams with up to 20 responses per phrase. The probability of the responded word and the conditional probability of the response given the context were predictive of the frequency with which each response was produced. Furthermore the order in which the participants generated multiple completions of the same context was predicted by the conditional probability as well. These results suggest that word choice in cloze tasks taps into implicit knowledge of a person’s past experience with that word in various contexts. Furthermore, the importance of n-gram conditional probabilities in our analysis is further evidence of implicit knowledge about multi-word sequences and support theories of language processing that involve anticipating or predicting based on context.


2020 ◽  
Vol 38 (Supplement) ◽  
pp. s208-s222
Author(s):  
Christian Unkelbach ◽  
Klaus Fiedler

Implicit measures are diagnostic tools to assess attitudes and evaluations that people cannot or may not want to report. Diagnostic inferences from such tools are subject to asymmetries. We argue that (causal) conditional probabilities p(AM+|A+) of implicitly measured attitudes AM+ given the causal influence of existing attitudes A+ is typically higher than the reverse (diagnostic) conditional probability p(A+|AM+), due to non-evaluative influences on implicit measures. We substantiate this argument with evidence for non-evaluative influences on evaluative priming—specifically, similarity effects reflecting the higher similarity of positive than negative prime-target pairs; integrativity effects based on primes and targets’ potential to form meaningful semantic compounds; and congruity proportion effects that originate in individuals’ decisional strategies. We also cursorily discuss non-evaluative influences in the Implicit Association Test (IAT). These influences not only have implications for the evaluative priming paradigm in particular, but also highlight the intricacies of diagnostic inferences from implicit measures in general.


2017 ◽  
Vol 7 (1) ◽  
pp. 32-63 ◽  
Author(s):  
M. Keith Wright

This paper presents ideas for improved conditional probability assessment and improved expert systems consultations. It cautions that knowledge engineers may sometimes be imprecise when capturing causal information from experts: their elicitation questions may not distinguish between causal and correlational expertise. This paper shows why and how such models cannot support normative inferencing over conditional probabilities as if they were all based on frequencies in the long run. In some cases, these probabilities are instead causal theory-based judgments, and therefore are not traditional conditional probabilities. This paper argues that these should be processed as if they were causal strength probabilities or causal propensity probabilities. This paper reviews the literature on causal and probability judgment, and then presents a probabilistic inferencing model that integrates theory-based causal probabilities with frequency-based conditional probabilities. The paper also proposes guidelines for elicitation questions that knowledge engineers may use to avoid conflating causal theory-based judgment with frequency based judgment.


1982 ◽  
Vol 50 (2) ◽  
pp. 655-661 ◽  
Author(s):  
William B. Davidson

This study examined in 94 college students the relationship between field-dependence and impulsivity with two measures of each construct. Impulsivity was assessed both as a cognitive style and as a personal lifestyle with the Matching Familiar Figures Test and the Impulsivity Scale of the Personality Research Form. Field-dependence was assessed with the Embedded Figures Test and the Articulation-of-Body-Concept Scale for Human Figure Drawings. Partial correlation coefficients were computed between the measures controlling for the effects of general intellectual ability on some measures. Results indicated that (1) the two measures of field-dependence were significantly related to each other, (2) the two measures of impulsivity were not significantly related to each other, and (3) the measures of field-dependence were, for the most part, unrelated to measures of impulsivity.


2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 262-262
Author(s):  
Mariko Sakka ◽  
Ayumi Igarashi ◽  
Chie Fukui ◽  
Maiko Noguchi-Watanabe ◽  
Asa Inagaki ◽  
...  

Abstract While quality of life (QOL) is an important endpoint of homecare for persons with dementia (PWD), PWDs often have difficulty in articulating their QOL by themselves. Instead proxy-rating is often used. However, evidence is still scarce regarding to what extent proxy-ratings reflect actual QOL of PWDs. We examined the association between self-report QOL by PWDs and proxy-rated QOL. We conducted a questionnaire survey to PWDs who were 75 years and older, their family, and homecare nurse in charge of the PWD. Two measures were used: 1) a newly developed, 4-item self-report for QOL of PWDs, and 2) a standardized, 6-item proxy-rating dementia QOL scale. In the self-report, the PWD were asked about their daily mood or satisfaction in life in brief, easy-to-understand sentences. The self-reports and proxy-ratings were compared using intraclass correlation coefficients (ICC). Data from 382 PWDs, 248 family caregivers and 124 nurses were used. The mean age of PWD was 85.9 years and 60.5% were female. The proxy-rating by nurses were more strongly associated with self-reports, compared to the association between family proxy rating and self-reports (r = 0.351, p < .001; r = 0.236, p < .001, respectively). Proxy ratings by spouses and biological children were significantly associated with self-report (r = 0.257, p =.004; r =. 204, p = .006, respectively), while rating by children-in-law were not (r = 0.217, p = .160). Proxy-ratings may not be an appropriate substitute for self-report. Homecare nurses may evaluate the QOL of PWD better than their family caregiver.


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