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Financial Time Series Uncertainty: A Review of Probabilistic AI Applications
Journal of Economic Surveys, EarlyView.ABSTRACT Probabilistic machine learning models offer a distinct advantage over traditional deterministic approaches by quantifying both epistemic uncertainty (stemming from limited data or model knowledge) and aleatoric uncertainty (due to inherent randomness in the data), along with full distributional forecasts.
Sivert Eggen +4 more
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Do robots boost productivity? A quantitative meta‐study
Journal of Economic Surveys, EarlyView.ABSTRACT This meta‐study analyzes the productivity effects of industrial robots. More than 1800 estimates from 85 primary studies are collected. The meta‐analytic evidence suggests that robotization has so far provided, at best, a small boost to productivity. There is strong evidence of publication bias in the positive direction.
Florian Schneider
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A Theory of Leadership Meta‐Talk and the Talking‐Doing Gap
Journal of Management Studies, EarlyView.Abstract We identify managers' meta‐level talk about the positive purpose, meaning, and significance of their actions as an overlooked type of leadership behaviour and call it leadership meta‐talk. We outline why leadership meta‐talk is not necessarily truthful or deceptive, but selective and loosely coupled with leadership practice.
Thomas Fischer, Mats Alvesson
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A novel Naive Bayes model: Packaged Hidden Naive Bayes
2011 6th IEEE Joint International Information Technology and Artificial Intelligence Conference, 2011Naive Bayes classifier has good performance on many datasets, however, the performance is very poor on some datasets which have a strong correlation between attributes due to the conditional independence assumption is not always true in the real world. In the latest Hidden Naive Bayes (HNB) algorithm, each attribute corresponds to a hidden parent which
Yaguang Ji, Songnian Yu, Yafeng Zhang
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Collaboratively weighted naive Bayes
Knowledge and Information Systems, 2021Naive Bayes (NB) was once awarded as one of the top 10 data mining algorithms, but the unreliable probability estimation and the unrealistic attribute conditional independence assumption limit its performance. To alleviate these two primary weaknesses simultaneously, instance and attribute weighting has been recently proposed.
Huan Zhang, Liangxiao Jiang, Chaoqun Li
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2021
With the help of the Naive Bayes classifier, this master thesis attempts to generate a trading strategy that outperforms the returns of two selected stock markets, the German (DAX) and the American (S&P 500) stock market, over a period ranging from 2004 to 2019. Trading decisions are made based on the a-posteriori probabilities derived based on the
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With the help of the Naive Bayes classifier, this master thesis attempts to generate a trading strategy that outperforms the returns of two selected stock markets, the German (DAX) and the American (S&P 500) stock market, over a period ranging from 2004 to 2019. Trading decisions are made based on the a-posteriori probabilities derived based on the
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24th Pan-Hellenic Conference on Informatics, 2020
Over the years, many learners that take advantage of the Bayesian theory have been developed and proved to be both efficient and performant in terms of classification predictiveness. Hidden Naive Bayes is no exception since its polynomial complexity makes it a desired base classifier to conduct under Weakly Supervised Learning that, unlikely the ...
Vangjel Kazllarof, Sotiris B. Kotsiantis
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Over the years, many learners that take advantage of the Bayesian theory have been developed and proved to be both efficient and performant in terms of classification predictiveness. Hidden Naive Bayes is no exception since its polynomial complexity makes it a desired base classifier to conduct under Weakly Supervised Learning that, unlikely the ...
Vangjel Kazllarof, Sotiris B. Kotsiantis
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Less naive Bayes spai detection
2007 Information Theory and Applications Workshop, 2007We consider a binary classification problem with a feature vector of high dimensionality. Spam mail filters are a popular example hereof. A naive Bayes filter assumes conditional independence of the feature vector components. We use the context tree weighting method as an application of the minimum description length principle to allow for dependencies
Yang, Hongming +2 more
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Naives Bayes-Klassifikationsverfahren
2020Das naive Bayes-Verfahren ist ein weiteres Klassifikationsverfahren. Es wird zunachst fur jede Klasse die Wahrscheinlichkeit geschatzt, mit der ein Objekt zu dieser Klasse gehort, wobei die aus der Statistik bekannte Bayes-Formel fur bedingte Wahrscheinlichkeiten benutzt wird.
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Probabilistic Fuzzy Naive Bayes
2015 Brazilian Conference on Intelligent Systems (BRACIS), 2015Bayesian networks are probabilistic graphical models capable of modeling statistical uncertainty and are widely applied in many classification problems. Specifically, Naive Bayesian networks are largely used due to their simple, naive structure, while still producing precise results.
Gabriel Moura, Mauro Roisenberg
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