Relevant bibliographies by topics / Bayesian intelligence

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Bayesian intelligence'

Author: Grafiati
Published: 10 December 2022
Last updated: 31 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Bayesian intelligence.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Bayesian intelligence"

1

Zelterman, Daniel. "Bayesian Artificial Intelligence." Technometrics 47, no. 1 (2005): 101–2. http://dx.doi.org/10.1198/tech.2005.s836.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Ramoni, Marco F. "Bayesian Artificial Intelligence." Journal of the American Statistical Association 100, no. 471 (2005): 1096–97. http://dx.doi.org/10.1198/jasa.2005.s39.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

V. Jensen, Finn. "Bayesian Artificial Intelligence." Pattern Analysis and Applications 7, no. 2 (2004): 221–23. http://dx.doi.org/10.1007/s10044-004-0214-5.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Vreeswijk, Gerard A. W. "Book Review: Bayesian Artificial Intelligence." Artificial Intelligence and Law 11, no. 4 (2003): 289–98. http://dx.doi.org/10.1023/b:arti.0000045970.25670.25.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Pascual-Garcia, Erica, and Guillermo De la Torre-Gea. "Bayesian Analysis to the experiences of corruption through Artificial Intelligence." International Journal of Trend in Scientific Research and Development Volume-2, Issue-2 (2018): 103–7. http://dx.doi.org/10.31142/ijtsrd2443.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Geetha, Dr V., Dr C. K. Gomathy, Mr S. Aravind, and V. Venkata Surya. "UNDERSTANDING BAYES RULE: BAYESIAN NETWORKS IN ARTIFICIAL INTELLIGENCE." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 11 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem27093.

Full text
Abstract:
Bayes' Rule and Bayesian Networks are foundational elements of AI, enabling probabilistic reasoning and informed decision-making in uncertain domains. This article introduces the core concepts and practical applications of these tools. We explore the historical origins, step-by-step construction of Bayesian Networks, and real-world AI applications. By understanding Bayes' Rule and Bayesian Networks, readers can unlock their potential to tackle complex AI challenges and uncertainties. And this article underscores the undeniable importance of Bayes' Rule and Bayesian Networks in AI. We hope to i
APA, Harvard, Vancouver, ISO, and other styles
7

Muhsina, Elvanisa Ayu, and Nurochman Nurochman. "SISTEM PAKAR REKOMENDASI PROFESI BERDASARKAN MULTIPLE INTELLIGENCES MENGGUNAKAN TEOREMA BAYESIAN." JISKA (Jurnal Informatika Sunan Kalijaga) 2, no. 1 (2017): 16. http://dx.doi.org/10.14421/jiska.2017.21-03.

Full text
Abstract:
Intelligence is perhaps to be the one of the most logical way to determine how smart people is. That fact has always been a problem at job because there are number of job that attract people but require a high GPA for them. Employee with high GPA doesn’t always fit in his skill and work role. They unable to understand and maintain their performance. This expert system is a necessary for recommend job using Intelligence. This research use a Bayesian theorem calculation to find out probability value and job recommendation. The value of MI (Multiple Intelligences)’s user, MI probability to a job
APA, Harvard, Vancouver, ISO, and other styles
8

TERZIYAN, VAGAN. "A BAYESIAN METANETWORK." International Journal on Artificial Intelligence Tools 14, no. 03 (2005): 371–84. http://dx.doi.org/10.1142/s0218213005002156.

Full text
Abstract:
Bayesian network (BN) is known to be one of the most solid probabilistic modeling tools. The theory of BN provides already several useful modifications of a classical network. Among those there are context-enabled networks such as multilevel networks or recursive multinets, which can provide separate BN modelling for different combinations of contextual features' values. The main challenge of this paper is the multilevel probabilistic meta-model (Bayesian Metanetwork), which is an extension of traditional BN and modification of recursive multinets. It assumes that interoperability between comp
APA, Harvard, Vancouver, ISO, and other styles
9

Pate-Cornell, Elisabeth. "Fusion of Intelligence Information: A Bayesian Approach." Risk Analysis 22, no. 3 (2002): 445–54. http://dx.doi.org/10.1111/0272-4332.00056.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Angelopoulos, Nicos, and James Cussens. "Bayesian learning of Bayesian networks with informative priors." Annals of Mathematics and Artificial Intelligence 54, no. 1-3 (2008): 53–98. http://dx.doi.org/10.1007/s10472-009-9133-x.

Full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Bayesian intelligence"

1

Horsch, Michael C. "Dynamic Bayesian networks." Thesis, University of British Columbia, 1990. http://hdl.handle.net/2429/28909.

Full text
Abstract:
Given the complexity of the domains for which we would like to use computers as reasoning engines, an automated reasoning process will often be required to perform under some state of uncertainty. Probability provides a normative theory with which uncertainty can be modelled. Without assumptions of independence from the domain, naive computations of probability are intractible. If probability theory is to be used effectively in AI applications, the independence assumptions from the domain should be represented explicitly, and used to greatest possible advantage. One such representation is a
APA, Harvard, Vancouver, ISO, and other styles
2

Edgington, Padraic D. "Modular Bayesian filters." Thesis, University of Louisiana at Lafayette, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3712276.

Full text
Abstract:
<p> In this dissertation, I introduce modularization as a means of efficiently solving problems represented by dynamic Bayesian networks and study the properties and effects of modularization relative to traditional solutions. Modularizing a Bayesian filter allows its results to be calculated faster than a traditional Bayesian filter. Traditional Bayesian filters can have issues when large problems must be solved within a short period of time. Modularization addresses this issue by dividing the full problem into a set of smaller problems that can then be solved with separate Bayesian filter
APA, Harvard, Vancouver, ISO, and other styles
3

Hanif, A. "Computational intelligence sequential Monte Carlos for recursive Bayesian estimation." Thesis, University College London (University of London), 2013. http://discovery.ucl.ac.uk/1403732/.

Full text
Abstract:
Recursive Bayesian estimation using sequential Monte Carlos methods is a powerful numerical technique to understand latent dynamics of non-linear non-Gaussian dynamical systems. Classical sequential Monte Carlos suffer from weight degeneracy which is where the number of distinct particles collapse. Traditionally this is addressed by resampling, which effectively replaces high weight particles with many particles with high inter-particle correlation. Frequent resampling, however, leads to a lack of diversity amongst the particle set in a problem known as sample impoverishment. Traditional seque
APA, Harvard, Vancouver, ISO, and other styles
4

Ross, Stéphane. "Model-based Bayesian reinforcement learning in complex domains." Thesis, McGill University, 2008. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=21960.

Full text
Abstract:
Reinforcement Learning has emerged as a useful framework for learning to perform a task optimally from experience in unknown systems. A major problem for such learning algorithms is how to balance optimally the exploration of the system, to gather knowledge, and the exploitation of current knowledge, to complete the task. Model-based Bayesian Reinforcement Learning (BRL) methods provide an optimal solution to this problem by formulating it as a planning problem under uncertainty. However, the complexity of these methods has so far limited their applicability to small and simple domains. To
APA, Harvard, Vancouver, ISO, and other styles
5

Gannon, Michael William. "Cruise missile proliferation : an application of Bayesian analysis to intelligence forecasting." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from the National Technical Information Service, 1992. http://handle.dtic.mil/100.2/ADA257717.

Full text
Abstract:
Thesis (M.S. in National Security Affairs) Naval Postgraduate School, September 1992.<br>Thesis advisor: Edward J. Laurance. ADA257717. "September 1992". Includes bibliographical reference (p. 82-84).
APA, Harvard, Vancouver, ISO, and other styles
6

Luo, Zhiyuan. "A probabilistic reasoning and learning system based on Bayesian belief networks." Thesis, Heriot-Watt University, 1992. http://hdl.handle.net/10399/1490.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Pomerantz, Daniel. "Designing a context dependant movie recommender: a hierarchical Bayesian approach." Thesis, McGill University, 2010. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=86751.

Full text
Abstract:
In this thesis, we analyze a context-dependent movie recommendation system using a Hierarchical Bayesian Network. Unlike most other recommender systems which either do not consider context or do so using collaborative filtering, our approach is content-based. This allows users to individually interpret contexts or invent their own contexts and continue to get good recommendations. By using a Hierarchical Bayesian Network, we can provide context recommendations when users have only provided a small amount of information about their preferences per context. At the same time, our model has enoug
APA, Harvard, Vancouver, ISO, and other styles
8

Carr, S. "Investigating the applicability of bayesian networks to the analysis of military intelligence." Thesis, Cranfield University, 2008. http://hdl.handle.net/1826/2826.

Full text
Abstract:
Intelligence failures have been attributed to an inability to correlate many small pieces of data into a larger picture. This thesis has sought to investigate how the fusion and analysis of uncertain or incomplete data through the use of Bayesian Belief Networks (BBN) compares with people’s intuitive judgements. These flexible, robust, graphical probabilistic networks are able to incorporate values from a wide range of sources including empirical values, experimental data and subjective values. Using the latter, elicited from a number of serving military officers, BBNs provide a logical framew
APA, Harvard, Vancouver, ISO, and other styles
9

Jaitha, Anant. "An Introduction to the Theory and Applications of Bayesian Networks." Scholarship @ Claremont, 2017. http://scholarship.claremont.edu/cmc_theses/1638.

Full text
Abstract:
Bayesian networks are a means to study data. A Bayesian network gives structure to data by creating a graphical system to model the data. It then develops probability distributions over these variables. It explores variables in the problem space and examines the probability distributions related to those variables. It conducts statistical inference over those probability distributions to draw meaning from them. They are good means to explore a large set of data efficiently to make inferences. There are a number of real world applications that already exist and are being actively researched. Th
APA, Harvard, Vancouver, ISO, and other styles
10

Saini, Nishrith. "Using Machine Intelligence to Prioritise Code Review Requests." Thesis, Blekinge Tekniska Högskola, Institutionen för programvaruteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-20140.

Full text
Abstract:
Background: Modern Code Review (MCR) is a process of reviewing code which is a commonly used practice in software development. It is the process of reviewing any new code changes that need to be merged with the existing codebase. As a developer, one receives many code review requests daily that need to be reviewed. When the developer receives the review requests, they are not prioritised. Manuallyprioritising them is a challenging and time-consuming process. Objectives: This thesis aims to address and solve the above issues by developing a machine intelligence-based code review prioritisation
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "Bayesian intelligence"

1

E, Nicholson Ann, ed. Bayesian artificial intelligence. 2nd ed. CRC Press, 2011.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
2

E, Nicholson Ann, ed. Bayesian artificial intelligence. Chapman & Hall/CRC, 2004.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
3

Research Institute for Advanced Computer Science (U.S.), ed. Bayesian learning. Research Institute for Advanced Computer Science, NASA Ames Research Center, 1989.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
4

Dowe, David L., ed. Algorithmic Probability and Friends. Bayesian Prediction and Artificial Intelligence. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-44958-1.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Neal, Radford M. Bayesian learning for neural networks. University of Toronto, Dept. of Computer Science, 1995.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
6

Barber, David. Bayesian reasoning and machine learning. Cambridge University Press, 2011.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
7

Szeliski, Richard. Bayesian Modeling of Uncertainty in Low-Level Vision. Springer US, 1989.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
8

Neal, Radford M. Bayesian learning for neural networks. Springer, 1996.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
9

Williamson, Jon. Bayesian nets and causality: Philosophical and computational foundations. Oxford University Press, 2005.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
10

E, Holmes Dawn, Jain L. C, and SpringerLink (Online service), eds. Innovations in Bayesian Networks: Theory and Applications. Springer-Verlag Berlin Heidelberg, 2008.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Book chapters on the topic "Bayesian intelligence"

1

Lu, Chenguang. "From Bayesian Inference to Logical Bayesian Inference." In Intelligence Science II. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01313-4_2.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Liu, Zhen “Leo.” "Bayesian Algorithms." In Artificial Intelligence for Engineers. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-75953-6_6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Lu, Chenguang. "Correction to: From Bayesian Inference to Logical Bayesian Inference." In Intelligence Science II. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01313-4_51.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Das, Monidipa, and Soumya K. Ghosh. "Spatial Bayesian Network." In Studies in Computational Intelligence. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-27749-9_4.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Das, Monidipa, and Soumya K. Ghosh. "Semantic Bayesian Network." In Studies in Computational Intelligence. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-27749-9_5.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Maragoudakis, Manolis, and Nikos Fakotakis. "Bayesian Feature Construction." In Advances in Artificial Intelligence. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11752912_25.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Conati, Cristina. "Bayesian Student Modeling." In Studies in Computational Intelligence. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14363-2_14.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Bhattacharjee, Shrutilipi, Soumya Kanti Ghosh, and Jia Chen. "Fuzzy Bayesian Semantic Kriging." In Studies in Computational Intelligence. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8664-0_4.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Nguyen, Thanh Dai, Sunil Gupta, Santu Rana, et al. "Cascade Bayesian Optimization." In AI 2016: Advances in Artificial Intelligence. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-50127-7_22.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Hutter, Marcus, David Quarel, and Elliot Catt. "Bayesian Sequence Prediction." In An Introduction to Universal Artificial Intelligence. Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003460299-5.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Bayesian intelligence"

1

Querlioz, Damien. "Computing with physics: the Bayesian approach." In Emerging Topics in Artificial Intelligence (ETAI) 2024, edited by Giovanni Volpe, Joana B. Pereira, Daniel Brunner, and Aydogan Ozcan. SPIE, 2024. http://dx.doi.org/10.1117/12.3028627.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Fang, Haopan, Wenxin Xiao, Yufeng Wu, and He Xiao. "Control optimization of manipulator by introducing Bayesian polynomial interpolation." In 2024 4th International Symposium on Artificial Intelligence and Intelligent Manufacturing (AIIM). IEEE, 2024. https://doi.org/10.1109/aiim64537.2024.10934163.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Macé, Maxime, Tassadit Amghar, Paul Richard, and Emmanuelle Ménétrier. "Renyi Entropy Search for Bayesian Optimization." In 2024 IEEE 36th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2024. https://doi.org/10.1109/ictai62512.2024.00115.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Wang, Zhifang, Na Wei, Zhengbin Zhai, and Huai Su. "Ultrasonic flowmeter fault early warning method based on Bayesian optimization XGBoost." In 2024 4th International Symposium on Artificial Intelligence and Intelligent Manufacturing (AIIM). IEEE, 2024. https://doi.org/10.1109/aiim64537.2024.10934315.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Skinner, Leete T., and Marc Johnson. "Bayesian networks for interpretable and extensible multisensor fusion." In Artificial Intelligence for Security and Defence Applications II, edited by Henri Bouma, Yitzhak Yitzhaky, Radhakrishna Prabhu, and Hugo J. Kuijf. SPIE, 2024. http://dx.doi.org/10.1117/12.3028532.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Prokopchina, Svetlana, and Veronika Zaslavskaia. "Methodology of Measurement Intellectualization based on Regularized Bayesian Approach in Uncertain Conditions." In 9th International Conference on Artificial Intelligence and Applications. Academy & Industry Research Collaboration Center, 2023. http://dx.doi.org/10.5121/csit.2023.131805.

Full text
Abstract:
Modern measurement tasks are confronted with inherent uncertainty. This significant uncertainty arises due to incomplete and imprecise knowledge about the models of measurement objects, influencing factors, measurement conditions, and the diverse nature of experimental data. This article provides a concise overview of the historical development of methodologies aimed at intellectualizing measurement processes in the context of uncertainty. It also discusses the classification of measurements and measurement systems. Furthermore, the fundamental requirements for intelligent measurement systems
APA, Harvard, Vancouver, ISO, and other styles
7

Takeishi, Naoya, Yoshinobu Kawahara, Yasuo Tabei, and Takehisa Yairi. "Bayesian Dynamic Mode Decomposition." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/392.

Full text
Abstract:
Dynamic mode decomposition (DMD) is a data-driven method for calculating a modal representation of a nonlinear dynamical system, and it has been utilized in various fields of science and engineering. In this paper, we propose Bayesian DMD, which provides a principled way to transfer the advantages of the Bayesian formulation into DMD. To this end, we first develop a probabilistic model corresponding to DMD, and then, provide the Gibbs sampler for the posterior inference in Bayesian DMD. Moreover, as a specific example, we discuss the case of using a sparsity-promoting prior for an automatic de
APA, Harvard, Vancouver, ISO, and other styles
8

Shen, Gehui, Xi Chen, and Zhihong Deng. "Variational Learning of Bayesian Neural Networks via Bayesian Dark Knowledge." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/282.

Full text
Abstract:
Bayesian neural networks (BNNs) have received more and more attention because they are capable of modeling epistemic uncertainty which is hard for conventional neural networks. Markov chain Monte Carlo (MCMC) methods and variational inference (VI) are two mainstream methods for Bayesian deep learning. The former is effective but its storage cost is prohibitive since it has to save many samples of neural network parameters. The latter method is more time and space efficient, however the approximate variational posterior limits its performance. In this paper, we aim to combine the advantages of
APA, Harvard, Vancouver, ISO, and other styles
9

Fortier, Nathan, John Sheppard, and Karthik Ganesan Pillai. "Bayesian abductive inference using overlapping swarm intelligence." In 2013 IEEE Symposium on Swarm Intelligence (SIS). IEEE, 2013. http://dx.doi.org/10.1109/sis.2013.6615188.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Fortier, Nathan, John Sheppard, and Shane Strasser. "Learning Bayesian classifiers using overlapping swarm intelligence." In 2014 IEEE Symposium On Swarm Intelligence (SIS). IEEE, 2014. http://dx.doi.org/10.1109/sis.2014.7011796.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Bayesian intelligence"

1

Pasupuleti, Murali Krishna. Stochastic Computation for AI: Bayesian Inference, Uncertainty, and Optimization. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv325.

Full text
Abstract:
Abstract: Stochastic computation is a fundamental approach in artificial intelligence (AI) that enables probabilistic reasoning, uncertainty quantification, and robust decision-making in complex environments. This research explores the theoretical foundations, computational techniques, and real-world applications of stochastic methods, focusing on Bayesian inference, Monte Carlo methods, stochastic optimization, and uncertainty-aware AI models. Key topics include probabilistic graphical models, Markov Chain Monte Carlo (MCMC), variational inference, stochastic gradient descent (SGD), and Bayes
APA, Harvard, Vancouver, ISO, and other styles
2

Pasupuleti, Murali Krishna. Mathematical Modeling for Machine Learning: Theory, Simulation, and Scientific Computing. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv125.

Full text
Abstract:
Abstract Mathematical modeling serves as a fundamental framework for advancing machine learning (ML) and artificial intelligence (AI) by integrating theoretical, computational, and simulation-based approaches. This research explores how numerical optimization, differential equations, variational inference, and scientific computing contribute to the development of scalable, interpretable, and efficient AI systems. Key topics include convex and non-convex optimization, physics-informed machine learning (PIML), partial differential equation (PDE)-constrained AI, and Bayesian modeling for uncertai
APA, Harvard, Vancouver, ISO, and other styles
3

Pasupuleti, Murali Krishna. Quantum Cognition: Modeling Decision-Making with Quantum Theory. National Education Services, 2025. https://doi.org/10.62311/nesx/rrvi225.

Full text
Abstract:
Abstract Quantum cognition applies quantum probability theory and mathematical principles from quantum mechanics to model human decision-making, reasoning, and cognitive processes beyond the constraints of classical probability models. Traditional decision theories, such as expected utility theory and Bayesian inference, struggle to explain context-dependent reasoning, preference reversals, order effects, and cognitive biases observed in human behavior. By incorporating superposition, interference, and entanglement, quantum cognitive models offer a probabilistic framework that better accounts
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Bayesian intelligence' for particular source types:
Journal articles Dissertations / Theses Books
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography