scholarly journals Surprise!

2020 ◽  
Author(s):  
Stephen R Cole ◽  
Jessie K Edwards ◽  
Sander Greenland
Keyword(s):  
Noisy Data ◽  
Information Value ◽  
Shannon Information ◽  
Limited Information ◽  
P Values ◽  
Interval Estimates ◽  
Information Measures

Abstract Measures of information and surprise, such as the Shannon information value (S value), quantify the signal present in a stream of noisy data. We illustrate the use of such information measures in the context of interpreting P values as compatibility indices. S values help communicate the limited information supplied by conventional statistics and cast a critical light on cutoffs used to judge and construct those statistics. Misinterpretations of statistics may be reduced by interpreting P values and interval estimates using compatibility concepts and S values instead of “significance” and “confidence.”

Neural Representation of Information Measure in the Primate Premotor Cortex

2006 ◽  
Vol 96 (1) ◽  
pp. 478-485 ◽  
Author(s):  
Kiyohiko Nakamura
Keyword(s):  
Premotor Cortex ◽  
Information Value ◽  
Neural Representation ◽  
Information Measure ◽  
Limited Information ◽  
Information Theoretic ◽  
Information Measures ◽  
Independent Events ◽  
To Receive ◽  
The Brain

Animals seek information to reduce their efforts to receive rewards and perform actions that enable them to gain more information. The ability of seeking information subserves higher cognition processes such as planning and reasoning. There exists limited information on how the brain measures and seeks information. In this study, I discuss results indicating that the brain quantifies information by using the information-theoretic measure. The monkeys were trained to perform saccadic eye movement to one of the visual targets. When required to choose from the targets that included varying amounts of information regarding the goal, the animals selected the most informative target. While making a choice, the neurons in the dorsal premotor cortex exhibited activity that reflected the corresponding information value. The population response of these neurons was examined using the following three measures: the information-theoretic measure, probability gain, and absolute change in beliefs. Changes in this response exhibited relatively similar proportionality to the three measures. An analysis of two intuitive conditions for information measures, decreasing monotonicity on probability and additivity between independent events, showed that only the information-theoretic measure satisfies both the conditions. These results suggest that in comparison with the other measures, the information-theoretic measure is more plausible for information measure in the brain.

scholarly journals Evaluating the Fit of Sequential G-DINA Model Using Limited-Information Measures

2019 ◽  
Vol 44 (3) ◽  
pp. 167-181 ◽  
Author(s):  
Wenchao Ma
Keyword(s):  
Goodness Of Fit ◽  
Type I Error ◽  
Model Simulation ◽  
Real Data ◽  
Error Rates ◽  
Type I ◽  
Limited Information ◽  
Detection Rates ◽  
Root Mean Square Residual ◽  
Information Measures

Limited-information fit measures appear to be promising in assessing the goodness-of-fit of dichotomous response cognitive diagnosis models (CDMs), but their performance has not been examined for polytomous response CDMs. This study investigates the performance of the Mord statistic and standardized root mean square residual (SRMSR) for an ordinal response CDM—the sequential generalized deterministic inputs, noisy “and” gate model. Simulation studies showed that the Mord statistic had well-calibrated Type I error rates, but the correct detection rates were influenced by various factors such as item quality, sample size, and the number of response categories. In addition, the SRMSR was also influenced by many factors and the common practice of comparing the SRMSR against a prespecified cut-off (e.g., .05) may not be appropriate. A set of real data was analyzed as well to illustrate the use of Mord statistic and SRMSR in practice.

APPLYING k-SEPARABILITY TO COLLABORATIVE RECOMMENDER SYSTEMS

2012 ◽  
Vol 21 (01) ◽  
pp. 1250001
Author(s):  
GEORGIOS ALEXANDRIDIS ◽  
GEORGIOS SIOLAS ◽  
ANDREAS STAFYLOPATIS
Keyword(s):  
Recommender Systems ◽  
Network Architecture ◽  
Noisy Data ◽  
Singular Value ◽  
User Preferences ◽  
Limited Information ◽  
Neural Network Architecture ◽  
Artificial Neural Network Architecture ◽  
Value Decomposition ◽  
User Ratings

Most recommender systems have too many items to propose to too many users based on limited information. This problem is formally known as the sparsity of the ratings' matrix, because this is the structure that holds user preferences. This paper outlines a Collaborative Filtering Recommender System that tries to amend this situation. After applying Singular Value Decomposition to reduce the dimensionality of the data, our system makes use of a dynamic Artificial Neural Network architecture with boosted learning to predict user ratings. Furthermore we use the concept of k-separability to deal with the resulting noisy data, a methodology not yet tested in Recommender Systems. The combination of these techniques applied to the MovieLens datasets seems to yield promising results.

scholarly journals Approximating Information Measures for Fields

Entropy ◽  
2020 ◽  
Vol 22 (1) ◽  
pp. 79 ◽  
Author(s):  
Łukasz Dębowski
Keyword(s):  
Natural Language ◽  
Statistical Modeling ◽  
Chain Rule ◽  
Shannon Information ◽  
Independent Interest ◽  
Stationary Processes ◽  
Ergodic Decomposition ◽  
Information Measures

We supply corrected proofs of the invariance of completion and the chain rule for the Shannon information measures of arbitrary fields, as stated by Dębowski in 2009. Our corrected proofs rest on a number of auxiliary approximation results for Shannon information measures, which may be of an independent interest. As also discussed briefly in this article, the generalized calculus of Shannon information measures for fields, including the invariance of completion and the chain rule, is useful in particular for studying the ergodic decomposition of stationary processes and its links with statistical modeling of natural language.

INDEPENDENCE AND CONDITIONAL DECOMPOSABLE INFORMATION MEASURES

2010 ◽  
Vol 18 (03) ◽  
pp. 225-246
Author(s):  
GIUSEPPE BUSANELLO ◽  
GIULIANELLA COLETTI ◽  
BARBARA VANTAGGI
Keyword(s):  
Information Measure ◽  
Shannon Information ◽  
Information Measures ◽  
Conditional Events

We deal with conditional decomposable information measures, directly defined as functions on a suitable set of conditional events satisfying a class of axioms. For these general measures we introduce a notion of independence and study its main properties in order to compare it with classical definitions present in the literature. The particular case of Wiener-Shannon information measure is taken in consideration and the links between the provided independence for information measures and the independence for the underlying probability are analyzed.

Usefulness of data on albumin and prealbumin concentrations in determining effectiveness of nutritional support.

1989 ◽  
Vol 35 (2) ◽  
pp. 271-274 ◽  
Author(s):  
L H Bernstein ◽  
C J Leukhardt-Fairfield ◽  
W Pleban ◽  
R Rudolph
Keyword(s):  
Nutritional Status ◽  
Reference Values ◽  
Parental Support ◽  
Nutritional Support ◽  
Shannon Information ◽  
Ongoing Study ◽  
Information Measures

Abstract In this ongoing study, albumin and prealbumin (transthyretin) changes were compared in 40 patients managed with enteral and (or) parental support with attainment of caloric/protein goals. The concentration of prealbumin in serum changed rapidly and more accurately reflected current nutritional status of these patients than did that of albumin. We determined concentrations of albumin and prealbumin that reflected significant improvement in nutritional status, using Rudolph's approach based on Shannon information measures. Reference values for albumin and prealbumin in the treatment populations were 25 g/L and 107 mg/L, respectively. A prealbumin concentration of 135 mg/L or greater reflected a return to stable status.

scholarly journals The Information Loss of a Stochastic Map

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 1021
Author(s):  
James Fullwood ◽  
Arthur J. Parzygnat
Keyword(s):  
Conditional Entropy ◽  
Information Loss ◽  
Shannon Information ◽  
Bayes Rule ◽  
Information Theoretic ◽  
Information Measures ◽  
Related Information ◽  
Conditional Information ◽  
Stochastic Map

We provide a stochastic extension of the Baez–Fritz–Leinster characterization of the Shannon information loss associated with a measure-preserving function. This recovers the conditional entropy and a closely related information-theoretic measure that we call conditional information loss. Although not functorial, these information measures are semi-functorial, a concept we introduce that is definable in any Markov category. We also introduce the notion of an entropic Bayes’ rule for information measures, and we provide a characterization of conditional entropy in terms of this rule.

scholarly journals Examining Drivers of Brand Community Engagement: The Moderation of Product, Brand and Consumer Characteristics

2019 ◽  
Vol 11 (17) ◽  
pp. 4672
Author(s):  
Jintang Wang ◽  
Junyun Liao ◽  
Shiyong Zheng ◽  
Biqing Li
Keyword(s):  
Community Engagement ◽  
Information Value ◽  
Social Value ◽  
Limited Information ◽  
Brand Community ◽  
Product Complexity ◽  
Online Brand Communities ◽  
Social Motivations ◽  
Survey Responses ◽  
The Impact

Numerous firms operate online brand communities (OBCs) in order to build a close consumer–brand relationship. To succeed in realizing this aim, firms must first sustain members’ brand community engagement. While prior studies have examined a series of drivers of brand community engagement, most of them focused on psychological and social motivations. Limited information is available about the role of product, brand and consumer characteristics in driving brand community engagement. Building on the uses and gratifications (UG) theory, the authors investigate the moderation of product complexity, brand symbolism, and extraversion in the relationship between brand community gratification and brand community engagement. With the collaboration of an online shopping site, 462 validated survey responses were collected to test our hypotheses. The results indicate that product complexity positively moderates the impact of information value on brand community engagement and brand symbolism positively moderates the effect of social value on brand community engagement. Finally, the results show that extraversion positively interacts with social value in enhancing brand community engagement. This study advances the understanding of brand community engagement.

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