Готові списки джерел за темами / Probability learning

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Добірка наукової літератури з теми "Probability learning"

Автор: Grafiati
Опубліковано: 4 червня 2021
Оновлено: 28 липня 2024

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Статті в журналах з теми "Probability learning"

1

SAEKI, Daisuke. "Probability learning in golden hamsters." Japanese Journal of Animal Psychology 49, no. 1 (1999): 41–47. http://dx.doi.org/10.2502/janip.49.41.

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2

Groth, Randall E., Jennifer A. Bergner, and Jathan W. Austin. "Dimensions of Learning Probability Vocabulary." Journal for Research in Mathematics Education 51, no. 1 (January 2020): 75–104. http://dx.doi.org/10.5951/jresematheduc.2019.0008.

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Анотація:
Normative discourse about probability requires shared meanings for disciplinary vocabulary. Previous research indicates that students’ meanings for probability vocabulary often differ from those of mathematicians, creating a need to attend to developing students’ use of language. Current standards documents conflict in their recommendations about how this should occur. In the present study, we conducted microgenetic research to examine the vocabulary use of four students before, during, and after lessons from a cycle of design-based research attending to probability vocabulary. In characterizi
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3

Groth, Randall E., Jennifer A. Bergner, and Jathan W. Austin. "Dimensions of Learning Probability Vocabulary." Journal for Research in Mathematics Education 51, no. 1 (January 2020): 75–104. http://dx.doi.org/10.5951/jresematheduc.51.1.0075.

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Анотація:
Normative discourse about probability requires shared meanings for disciplinary vocabulary. Previous research indicates that students’ meanings for probability vocabulary often differ from those of mathematicians, creating a need to attend to developing students’ use of language. Current standards documents conflict in their recommendations about how this should occur. In the present study, we conducted microgenetic research to examine the vocabulary use of four students before, during, and after lessons from a cycle of design-based research attending to probability vocabulary. In characterizi
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4

Rivas, Javier. "Probability matching and reinforcement learning." Journal of Mathematical Economics 49, no. 1 (January 2013): 17–21. http://dx.doi.org/10.1016/j.jmateco.2012.09.004.

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5

West, Bruce J. "Fractal Probability Measures of Learning." Methods 24, no. 4 (August 2001): 395–402. http://dx.doi.org/10.1006/meth.2001.1208.

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6

Jiang, Xiaolei. "Conditional Probability in Machine Learning." Journal of Education and Educational Research 4, no. 2 (July 20, 2023): 31–33. http://dx.doi.org/10.54097/jeer.v4i2.10647.

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Анотація:
To help teaching of machine learning course, manipulation rules and application examples of conditional probabilities in machine learning are presented. The emphasis is to make a clear distinction between reasonable assumptions and logical deductions developed from assumptions and axioms. The formula for conditional probability of conditional probability is presented with examples in Bayesian coin tossing, Bayesian linear regression, and Gaussian processes for regression and classification. The signal + noise model is formulated in terms of a proposition and exemplified by linear-Gaussian mode
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7

Malley, J. D., J. Kruppa, A. Dasgupta, K. G. Malley, and A. Ziegler. "Probability Machines." Methods of Information in Medicine 51, no. 01 (2012): 74–81. http://dx.doi.org/10.3414/me00-01-0052.

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Анотація:
SummaryBackground: Most machine learning approaches only provide a classification for binary responses. However, probabilities are required for risk estimation using individual patient characteristics. It has been shown recently that every statistical learning machine known to be consistent for a nonparametric regression problem is a probability machine that is provably consistent for this estimation problem.Objectives: The aim of this paper is to show how random forests and nearest neighbors can be used for consistent estimation of individual probabilities.Methods: Two random forest algorithm
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8

Dawson, Michael R. W. "Probability Learning by Perceptrons and People." Comparative Cognition & Behavior Reviews 15 (2022): 1–188. http://dx.doi.org/10.3819/ccbr.2019.140011.

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9

HIRASAWA, Kotaro, Masaaki HARADA, Masanao OHBAYASHI, Juuichi MURATA, and Jinglu HU. "Probability and Possibility Automaton Learning Network." IEEJ Transactions on Industry Applications 118, no. 3 (1998): 291–99. http://dx.doi.org/10.1541/ieejias.118.291.

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Groth, Randall E., Jaime Butler, and Delmar Nelson. "Overcoming challenges in learning probability vocabulary." Teaching Statistics 38, no. 3 (May 26, 2016): 102–7. http://dx.doi.org/10.1111/test.12109.

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Дисертації з теми "Probability learning"

1

Gozenman, Filiz. "Interaction Of Probability Learning And Working Memory." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614535/index.pdf.

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Анотація:
Probability learning is the ability to establish a relationship between stimulus and outcomes based on occurrence probabilities using repetitive feedbacks. Participants learn the task according to the cue-outcome relationship, and try to gain in depth understanding of this relationship throughout the experiment. While learning is at the highest level, people rely on their working memory. In this study 20 participants were presented a probability learning task, and their prefrontal cortex activity was measured with functional Near-Infrared Spectroscopy. It was hypothesized that as participants
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2

RYSZ, TERI. "METACOGNITION IN LEARNING ELEMENTARY PROBABILITY AND STATISTICS." University of Cincinnati / OhioLINK, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1099248340.

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3

Bouchacourt, Diane. "Task-oriented learning of structured probability distributions." Thesis, University of Oxford, 2017. https://ora.ox.ac.uk/objects/uuid:0665495b-afbb-483b-8bdf-cbc6ae5baeff.

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Анотація:
Machine learning models automatically learn from historical data to predict unseen events. Such events are often represented as complex multi-dimensional structures. In many cases there is high uncertainty in the prediction process. Research has developed probabilistic models to capture distributions of complex objects, but their learning objective is often agnostic of the evaluation loss. In this thesis, we address the aforementioned defficiency by designing probabilistic methods for structured object prediction that take into account the task at hand. First, we consider that the task at hand
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4

Li, Chengtao Ph D. Massachusetts Institute of Technology. "Diversity-inducing probability measures for machine learning." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/121724.

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Анотація:
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 163-176).<br>Subset selection problems arise in machine learning within kernel approximation, experimental design, and numerous other applications. In such applications, one often seeks to select diverse subsets of items to represent the population. One way to select such diverse subsets is to sample according to Diversity-Inducing Probability Measures (DIPMs) that assign higher probabilitie
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5

Hunt, Gareth David. "Reinforcement Learning for Low Probability High Impact Risks." Thesis, Curtin University, 2019. http://hdl.handle.net/20.500.11937/77106.

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Анотація:
We demonstrate a method of reinforcement learning that uses training in simulation. Our system generates an estimate of the potential reward and danger of each action as well as a measure of the uncertainty present in both. The system generates this by seeking out not only rewarding actions but also dangerous ones in the simulated training. During runtime our system is able to use this knowledge to avoid risks while accomplishing its tasks.
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6

Słowiński, Witold. "Autonomous learning of domain models from probability distribution clusters." Thesis, University of Aberdeen, 2014. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=211059.

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Анотація:
Nontrivial domains can be difficult to understand and the task of encoding a model of such a domain can be difficult for a human expert, which is one of the fundamental problems of knowledge acquisition. Model learning provides a way to address this problem by allowing a predictive model of the domain's dynamics to be learnt algorithmically, without human supervision. Such models can provide insight about the domain to a human or aid in automated planning or reinforcement learning. This dissertation addresses the problem of how to learn a model of a continuous, dynamic domain, from sensory obs
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7

Benson, Carol Trinko Jones Graham A. "Assessing students' thinking in modeling probability contexts." Normal, Ill. Illinois State University, 2000. http://wwwlib.umi.com/cr/ilstu/fullcit?p9986725.

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Анотація:
Thesis (Ph. D.)--Illinois State University, 2000.<br>Title from title page screen, viewed May 11, 2006. Dissertation Committee: Graham A. Jones (chair), Kenneth N. Berk, Patricia Klass, Cynthia W. Langrall, Edward S. Mooney. Includes bibliographical references (leaves 115-124) and abstract. Also available in print.
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8

Rast, Jeanne D. "A Comparison of Learning Subjective and Traditional Probability in Middle Grades." Digital Archive @ GSU, 2005. http://digitalarchive.gsu.edu/msit_diss/4.

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Анотація:
The emphasis given to probability and statistics in the K-12 mathematics curriculum has brought attention to the various approaches to probability and statistics concepts, as well as how to teach these concepts. Teachers from fourth, fifth, and sixth grades from a small suburban Catholic school engaged their students (n=87) in a study to compare learning traditional probability concepts to learning traditional and subjective probability concepts. The control group (n=44) received instruction in traditional probability, while the experimental group (n=43) received instruction in traditional and
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9

Lindsay, David George. "Machine learning techniques for probability forecasting and their practical evaluations." Thesis, Royal Holloway, University of London, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.445274.

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Kornfeld, Sarah. "Predicting Default Probability in Credit Risk using Machine Learning Algorithms." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-275656.

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Анотація:
This thesis has explored the field of internally developed models for measuring the probability of default (PD) in credit risk. As regulators put restrictions on modelling practices and inhibit the advance of risk measurement, the fields of data science and machine learning are advancing. The tradeoff between stricter regulation on internally developed models and the advancement of data analytics was investigated by comparing model performance of the benchmark method Logistic Regression for estimating PD with the machine learning methods Decision Trees, Random Forest, Gradient Boosting and Art
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Книги з теми "Probability learning"

1

Batanero, Carmen, Egan J. Chernoff, Joachim Engel, Hollylynne S. Lee, and Ernesto Sánchez. Research on Teaching and Learning Probability. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31625-3.

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DasGupta, Anirban. Probability for Statistics and Machine Learning. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9634-3.

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3

Aggarwal, Charu C. Probability and Statistics for Machine Learning. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-53282-5.

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4

Egan, J. Chernoff, Engel Joachim, Lee Hollylynne S, and Sánchez Ernesto, eds. Research on Teaching and Learning Probability. Cham: Springer, 2016.

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5

Unpingco, José. Python for Probability, Statistics, and Machine Learning. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18545-9.

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Unpingco, José. Python for Probability, Statistics, and Machine Learning. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30717-6.

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Unpingco, José. Python for Probability, Statistics, and Machine Learning. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04648-3.

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8

Powell, Warren B. Optimal learning. Hoboken, New Jersey: Wiley, 2012.

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9

Peck, Roxy. Statistics: Learning from data. Australia: Brooks/Cole, Cengage Learning, 2014.

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10

Knez, Igor. To know what to know before knowing: Acquisition of functional rules in probabilistic ecologies. Uppsala: Uppsala University, 1992.

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Частини книг з теми "Probability learning"

1

Glenberg, Arthur M., and Matthew E. Andrzejewski. "Probability." In Learning From Data, 105–19. 4th ed. New York: Routledge, 2024. http://dx.doi.org/10.4324/9781003025405-6.

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2

Zeugmann, Thomas, Pascal Poupart, James Kennedy, Xin Jin, Jiawei Han, Lorenza Saitta, Michele Sebag, et al. "Posterior Probability." In Encyclopedia of Machine Learning, 780. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_648.

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3

Zeugmann, Thomas, Pascal Poupart, James Kennedy, Xin Jin, Jiawei Han, Lorenza Saitta, Michele Sebag, et al. "Prior Probability." In Encyclopedia of Machine Learning, 782. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_658.

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4

Kumar Singh, Bikesh, and G. R. Sinha. "Probability Theory." In Machine Learning in Healthcare, 23–33. New York: CRC Press, 2022. http://dx.doi.org/10.1201/9781003097808-2.

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5

Unpingco, José. "Probability." In Python for Probability, Statistics, and Machine Learning, 35–100. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30717-6_2.

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6

Unpingco, José. "Probability." In Python for Probability, Statistics, and Machine Learning, 39–121. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18545-9_2.

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Unpingco, José. "Probability." In Python for Probability, Statistics, and Machine Learning, 47–134. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04648-3_2.

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Faul, A. C. "Probability Theory." In A Concise Introduction to Machine Learning, 7–61. Boca Raton, Florida : CRC Press, [2019] | Series: Chapman & Hall/CRC machine learning & pattern recognition: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9781351204750-2.

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Aggarwal, Charu C. "Probability Distributions." In Probability and Statistics for Machine Learning, 127–90. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-53282-5_4.

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10

Ghatak, Abhijit. "Probability and Distributions." In Machine Learning with R, 31–56. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6808-9_2.

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Тези доповідей конференцій з теми "Probability learning"

1

Temlyakov, V. N. "Optimal estimators in learning theory." In Approximation and Probability. Warsaw: Institute of Mathematics Polish Academy of Sciences, 2006. http://dx.doi.org/10.4064/bc72-0-23.

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2

Neville, Jennifer, David Jensen, Lisa Friedland, and Michael Hay. "Learning relational probability trees." In the ninth ACM SIGKDD international conference. New York, New York, USA: ACM Press, 2003. http://dx.doi.org/10.1145/956750.956830.

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3

Arieli, Itai, Yakov Babichenko, and Manuel Mueller-Frank. "Naive Learning Through Probability Matching." In EC '19: ACM Conference on Economics and Computation. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3328526.3329601.

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4

Sánchez, Emesta, Sibel Kazak, and Egan J. Chernoff. "Teaching and Learning of Probability." In The 14th International Congress on Mathematical Education. WORLD SCIENTIFIC, 2024. http://dx.doi.org/10.1142/9789811287152_0035.

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Ha, Ming-hu, Zhi-fang Feng, Er-ling Du, and Yun-chao Bai. "Further Discussion on Quasi-Probability." In 2006 International Conference on Machine Learning and Cybernetics. IEEE, 2006. http://dx.doi.org/10.1109/icmlc.2006.258542.

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Burgos, María, María Del Mar López-Martín, and Nicolás Tizón-Escamilla. "ALGEBRAIC REASONING IN PROBABILITY TASKS." In 14th International Conference on Education and New Learning Technologies. IATED, 2022. http://dx.doi.org/10.21125/edulearn.2022.0777.

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Herlau, Tue. "Active learning of causal probability trees." In 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2022. http://dx.doi.org/10.1109/icmla55696.2022.00193.

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Eugênio, Robson, Carlos Monteiro, Liliane Carvalho, José Roberto Costa Jr., and Karen François. "MATHEMATICS TEACHERS LEARNING ABOUT PROBABILITY LITERACY." In 14th International Technology, Education and Development Conference. IATED, 2020. http://dx.doi.org/10.21125/inted.2020.0272.

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Struski, Łukasz, Adam Pardyl, Jacek Tabor, and Bartosz Zieliński. "ProPML: Probability Partial Multi-label Learning." In 2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 2023. http://dx.doi.org/10.1109/dsaa60987.2023.10302620.

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Ramishetty, Sravani, and Abolfazl Hashemi. "High Probability Guarantees For Federated Learning." In 2023 59th Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2023. http://dx.doi.org/10.1109/allerton58177.2023.10313468.

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Звіти організацій з теми "Probability learning"

1

Shute, Valerie J., and Lisa A. Gawlick-Grendell. An Experimental Approach to Teaching and Learning Probability: Stat Lady. Fort Belvoir, VA: Defense Technical Information Center, April 1996. http://dx.doi.org/10.21236/ada316969.

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2

Ilyin, M. E. The distance learning course «Theory of probability, mathematical statistics and random functions». OFERNIO, December 2018. http://dx.doi.org/10.12731/ofernio.2018.23529.

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Kriegel, Francesco. Learning description logic axioms from discrete probability distributions over description graphs (Extended Version). Technische Universität Dresden, 2018. http://dx.doi.org/10.25368/2022.247.

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Анотація:
Description logics in their standard setting only allow for representing and reasoning with crisp knowledge without any degree of uncertainty. Of course, this is a serious shortcoming for use cases where it is impossible to perfectly determine the truth of a statement. For resolving this expressivity restriction, probabilistic variants of description logics have been introduced. Their model-theoretic semantics is built upon so-called probabilistic interpretations, that is, families of directed graphs the vertices and edges of which are labeled and for which there exists a probability measure o
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4

Kriegel, Francesco. Learning General Concept Inclusions in Probabilistic Description Logics. Technische Universität Dresden, 2015. http://dx.doi.org/10.25368/2022.220.

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Анотація:
Probabilistic interpretations consist of a set of interpretations with a shared domain and a measure assigning a probability to each interpretation. Such structures can be obtained as results of repeated experiments, e.g., in biology, psychology, medicine, etc. A translation between probabilistic and crisp description logics is introduced and then utilised to reduce the construction of a base of general concept inclusions of a probabilistic interpretation to the crisp case for which a method for the axiomatisation of a base of GCIs is well-known.
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5

Gribok, Andrei V., Kevin P. Chen, and Qirui Wang. Machine-Learning Enabled Evaluation of Probability of Piping Degradation In Secondary Systems of Nuclear Power Plants. Office of Scientific and Technical Information (OSTI), May 2020. http://dx.doi.org/10.2172/1634815.

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de Luis, Mercedes, Emilio Rodríguez, and Diego Torres. Machine learning applied to active fixed-income portfolio management: a Lasso logit approach. Madrid: Banco de España, September 2023. http://dx.doi.org/10.53479/33560.

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Анотація:
The use of quantitative methods constitutes a standard component of the institutional investors’ portfolio management toolkit. In the last decade, several empirical studies have employed probabilistic or classification models to predict stock market excess returns, model bond ratings and default probabilities, as well as to forecast yield curves. To the authors’ knowledge, little research exists into their application to active fixed-income management. This paper contributes to filling this gap by comparing a machine learning algorithm, the Lasso logit regression, with a passive (buy-and-hold)
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Dinarte, Lelys, Pablo Egaña del Sol, and Claudia Martínez. When Emotion Regulation Matters: The Efficacy of Socio-Emotional Learning to Address School-Based Violence in Central America. Inter-American Development Bank, March 2024. http://dx.doi.org/10.18235/0012854.

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Анотація:
After-school programs (ASP) that keep youth protected while engaging them in socio-emotional learning might address school-based violent behaviors. This paper experimentally studies the socio-emotional-learning component of an ASP targeted to teenagers in public schools in the most violent neighborhoods of El Salvador, Honduras, and Guatemala. Participant schools were randomly assigned to different ASP variations, some of them including psychology-based interventions. Results indicate that including psychology-based activities as part of the ASP increases by 23 percentage points the probabilit
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8

Moreno Pérez, Carlos, and Marco Minozzo. “Making Text Talk”: The Minutes of the Central Bank of Brazil and the Real Economy. Madrid: Banco de España, November 2022. http://dx.doi.org/10.53479/23646.

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Анотація:
This paper investigates the relationship between the views expressed in the minutes of the meetings of the Central Bank of Brazil’s Monetary Policy Committee (COPOM) and the real economy. It applies various computational linguistic machine learning algorithms to construct measures of the minutes of the COPOM. First, we create measures of the content of the paragraphs of the minutes using Latent Dirichlet Allocation (LDA). Second, we build an uncertainty index for the minutes using Word Embedding and K-Means. Then, we combine these indices to create two topic-uncertainty indices. The first one
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9

Robson, Jennifer. The Canada Learning Bond, financial capability and tax-filing: Results from an online survey of low and modest income parents. SEED Winnipeg/Carleton University Arthur Kroeger College of Public Affairs, March 2022. http://dx.doi.org/10.22215/clb20220301.

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Анотація:
Previous research has identified several likely causes of eligible non-participation in the Canada Learning Bond (CLB), including awareness, financial exclusion, and administrative barriers. This study expands on that research, with a particular focus on the role of tax-filing as an administrative obstacle to accessing the CLB. I present results from an online survey of low and modest income parents (n=466) conducted in 2021. We find that, even among parents reporting they have received the CLB (46%), a majority (51%) report low confidence in their familiarity with the program, and more than o
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10

Schiefelbein, Ernesto, Paulina Schiefelbein, and Laurence Wolff. Cost-Effectiveness of Education Policies in Latin America: A Survey of Expert Opinion. Inter-American Development Bank, December 1998. http://dx.doi.org/10.18235/0008789.

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Анотація:
This paper provides an alternative approach to measuring the cost-effectiveness of educational interventions. The authors devised a questionnaire and gave it to ten international experts, mainly located in universities and international agencies, all of whom were well acquainted with educational research and with practical attempts at educational reform in the region; as well as to about 30 Latin American planner/practitioners, most of them working in the planning office of their ministry of education. Each respondent was asked to estimate the impact of 40 possible primary school interventions
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