Search Results - "Learning and Inference in Bayesian Networks"

Minimax detection boundary and sharp optimal test for Gaussian graphical models

Authors: Yumou Qiu Bin Guo

Source: Journal of the Royal Statistical Society Series B: Statistical Methodology. 86:1221-1242

Subject Terms: Statistics and Probability, Learning and Inference in Bayesian Networks, Test (biology), Regularization and Variable Selection Methods, Mathematical analysis, Quantum mechanics, 01 natural sciences, Artificial Intelligence, 0502 economics and business, FOS: Mathematics, Minimax, 0101 mathematics, Physics, 4. Education, Mathematical optimization, 05 social sciences, Paleontology, Geology, FOS: Earth and related environmental sciences, Computer science, 3. Good health, Boundary (topology), Physical Sciences, Computer Science, Gaussian, Mathematics, Detection and Handling of Multicollinearity in Regression Analysis

Conditional probability table limit-based quantization for Bayesian networks: model quality, data fidelity and structure score: model quality, data fidelity and structure score

Authors: Rafael Rodrigues Mendes Ribeiro Jordão Natal Cassio P. de Campos et al.

Source: Applied Intelligence. 54:4668-4688

Subject Terms: Artificial intelligence, Learning and Inference in Bayesian Networks, 0207 environmental engineering, Social Sciences, 02 engineering and technology, Multi-Criteria Decision Making, Management Science and Operations Research, Process Fault Detection and Diagnosis in Industries, Bayesian probability, Decision Sciences, Engineering, Artificial Intelligence, Quantization, BIC, 0202 electrical engineering, electronic engineering, information engineering, FOS: Mathematics, Data mining, Statistics, Computer science, Bayesian network, Control and Systems Engineering, Computer Science, Physical Sciences, Fidelity, Telecommunications, CPT, Mathematics

Universal consistency of the k-NN rule in metric spaces and Nagata dimension. II

Authors: Sushma Kumari Vladimir Pestov

Source: ESAIM: Probability and Statistics. 28:132-160

Subject Terms: FOS: Computer and information sciences, Computer Science - Machine Learning, Metric (unit), Learning and Inference in Bayesian Networks, Economics, Separable space, Space (punctuation), Mathematical analysis, Metric Spaces, Machine Learning (cs.LG), Computational Complexity and Algorithmic Information Theory, Artificial Intelligence, Euclidean space, FOS: Mathematics, Active Learning in Machine Learning Research, Pure mathematics, Discrete mathematics, Minkowski–Bouligand dimension, Computer science, Packing dimension, Operating system, Dimension (graph theory), Operations management, Computational Theory and Mathematics, Computer Science, Physical Sciences, Metric space, Fractal dimension, Fractal, 62H30, 54F45, Mathematics, Consistency (knowledge bases)

Access URL: http://arxiv.org/abs/2305.17282

Sparse Variable Selection on High Dimensional Heterogeneous Data With Tree Structured Responses

Authors: Hui Liu Xiang Liu Jing Diao et al.

Source: IEEE Access, Vol 12, Pp 50779-50791 (2024)

Subject Terms: Statistics and Probability, FOS: Computer and information sciences, 0301 basic medicine, Artificial intelligence, Learning and Inference in Bayesian Networks, mixed model, Confounding factors, Regularization and Variable Selection Methods, Mathematical analysis, 01 natural sciences, Methodology (stat.ME), 03 medical and health sciences, Selection (genetic algorithm), Artificial Intelligence, Lasso (programming language), Biochemistry, Genetics and Molecular Biology, Microarray Data Analysis and Gene Expression Profiling, Machine learning, FOS: Mathematics, Confounding, Sparse Models, Feature Selection, 0101 mathematics, Variable (mathematics), Molecular Biology, Data mining, Statistics - Methodology, Model Selection, 0303 health sciences, genome-wide association study, Statistics, Pure mathematics, Life Sciences, Computer science, TK1-9971, World Wide Web, Dimension (graph theory), Variable Selection, Physical Sciences, Computer Science, Feature selection, Tree (set theory), Dependency (UML), Electrical engineering. Electronics. Nuclear engineering, High-Dimensional Data, Mathematics, variable selection

Access URL: http://arxiv.org/abs/1711.08265
https://doaj.org/article/1b1ef698156b4e45b7bc98012a462253

Explaining Query Answers in Probabilistic Databases

Authors: Hichem Debbi

Source: Re-Unir. Archivo Institucional de la Universidad Internacional de La Rioja
Universidad Internacional de La Rioja (UNIR)

Subject Terms: Artificial intelligence, causality, Data Stream Management Systems and Techniques, Learning and Inference in Bayesian Networks, Computer Networks and Communications, Trajectory Data Mining and Analysis, Probabilistic Databases, probabilistic databases, Complex Event Processing, Search engine, Data science, Database, Causal Discovery, Artificial Intelligence, Database query, Sargable, Information retrieval, Relational database, query answers, Query optimization, Relational Database Systems, Probabilistic logic, Probabilistic Learning, Database theory, IJIMAI, Probabilistic database, Computer science, Computer Science, Physical Sciences, Signal Processing, Web search query, conjunctive queries, explanation, Query language

Access URL: https://reunir.unir.net/handle/123456789/15133
https://doi.org/10.9781/ijimai.2023.07.005
https://reunir.unir.net/handle/123456789/15133

Prediction Using Many Samples with Models Possibly Containing Partially Shared Parameters

Authors: Xinyu Zhang Huihang Liu Yanzhang Wei et al.

Source: Journal of Business & Economic Statistics. 42:187-196

Subject Terms: Statistics and Probability, Artificial intelligence, Learning and Inference in Bayesian Networks, Mixture Models, Regularization and Variable Selection Methods, 01 natural sciences, FOS: Economics and business, Artificial Intelligence, Machine learning, 0502 economics and business, FOS: Mathematics, Econometrics, Sparse Models, 0101 mathematics, Data mining, Model Selection, Hidden Markov Models, 05 social sciences, Covariate, Predictive modelling, Structure Learning, Computer science, Physical Sciences, Computer Science, Model-Based Clustering with Mixture Models, Mathematics

Optimal Subsampling Bootstrap for Massive Data

Authors: Yingying Ma Chenlei Leng Hansheng Wang

Source: Journal of Business & Economic Statistics. 42:174-186

Subject Terms: FOS: Computer and information sciences, Statistics and Probability, Resampling, Artificial intelligence, Learning and Inference in Bayesian Networks, Regularization and Variable Selection Methods, Nonparametric Methods, Estimator, 01 natural sciences, Methodology (stat.ME), Gaussian Processes in Machine Learning, FOS: Economics and business, Inference, Artificial Intelligence, Machine learning, FOS: Mathematics, Econometrics, 0101 mathematics, Data mining, Statistics - Methodology, Model Selection, Hyperparameter, Statistics, Measure (data warehouse), Computer science, Bootstrapping (finance), Variable Selection, Physical Sciences, Computer Science, Mathematics, Statistical inference

Access URL: http://arxiv.org/abs/2302.07533

SEAG: Structure-Aware Event Causality Generation

Authors: Zhengwei Tao Zhi Jin Xi Bai et al.

Source: Findings of the Association for Computational Linguistics: ACL 2023. :4631-4644

Subject Terms: FOS: Computer and information sciences, Artificial intelligence, Learning and Inference in Bayesian Networks, FOS: Political science, Software Defect Prediction, FOS: Law, Quantum mechanics, Graph, Systems engineering, Causal Discovery, Task (project management), Identification (biology), Theoretical computer science, Engineering, Artificial Intelligence, Machine learning, Event (particle physics), Political science, Biology, Natural Language Processing, Topic Modeling, Natural language processing, Physics, Politics, Botany, Discriminative model, Structure Learning, Computer science, Bug Localization, Causality (physics), Computer Science, Physical Sciences, Representation (politics), Law, Empirical Studies in Software Engineering, Information Systems

Multi-Relational Probabilistic Event Representation Learning via Projected Gaussian Embedding

Authors: Linhai Zhang C.L. Zhang Deyu Zhou

Source: Findings of the Association for Computational Linguistics: ACL 2023. :6162-6174

Subject Terms: Data Quality Assessment and Improvement, Relation (database), Artificial intelligence, Learning and Inference in Bayesian Networks, Sequence-to-Sequence Learning, FOS: Political science, Social Sciences, FOS: Law, Management Science and Operations Research, Quantum mechanics, Decision Sciences, Theoretical computer science, Artificial Intelligence, Machine learning, Relational database, Statistical relational learning, Event (particle physics), Data mining, Political science, Natural Language Processing, Probabilistic logic, Probabilistic Learning, Topic Modeling, Physics, 4. Education, Politics, Feature learning, Semantics (computer science), Optics, Structure Learning, Focus (optics), Named Entity Recognition, Computer science, Programming language, Computer Science, Physical Sciences, Representation (politics), Law, Embedding

Graph-Assisted Bayesian Node Classifiers

Authors: Hakim Hafidi Philippe Ciblat Mounir Ghogho et al.

Source: IEEE Access, Vol 11, Pp 23989-24002 (2023)

Subject Terms: attributed graphs, Graphical Models, Artificial intelligence, Learning and Inference in Bayesian Networks, 02 engineering and technology, Bayesian probability, Pattern recognition (psychology), Graph, Node classification, Theoretical computer science, Artificial Intelligence, Graph Convolutional Networks, Machine learning, 0202 electrical engineering, electronic engineering, information engineering, Graph Neural Networks, Signal Processing on Graphs, Data mining, 4. Education, Knowledge Graph Embedding, degree of impurity, Statistical and Nonlinear Physics, Computer science, TK1-9971, Bayesian framework, Physics and Astronomy, Computer Science, Physical Sciences, Statistical Mechanics of Complex Networks, Electrical engineering. Electronics. Nuclear engineering, Graph Neural Network Models and Applications, Classifier (UML)

Access URL: https://doaj.org/article/f60ff0f015164a0e8770844679ffd5c5

Assessment of Covariance Selection Methods in High-Dimensional Gaussian Graphical Models

Authors: J. Maldonado S. M. Ruiz

Source: Trends in Computational and Applied Mathematics, Vol 23, Iss 3 (2022)

Subject Terms: Statistics and Probability, Graphical Models, Artificial intelligence, Glasso, Learning and Inference in Bayesian Networks, Covariance matrix, Geometry, Diagonal, Covariance function, Model selection, Estimator, Quantum mechanics, 01 natural sciences, Graph, Selection (genetic algorithm), Artificial Intelligence, QA1-939, FOS: Mathematics, Estimation of covariance matrices, 0101 mathematics, Probabilistic Graphical Models, Covariance selection, Covariance, Physics, 4. Education, Statistics, 16. Peace & justice, Computer science, Algorithm, Gaussian graphical model, Combinatorics, Computer Science, Physical Sciences, Gaussian, Mathematics, Graphical model, Detection and Handling of Multicollinearity in Regression Analysis

Access URL: https://doaj.org/article/193f3dbe2f5746c9ab0d13cc76455d45

Model averaging for sparse seemingly unrelated regression using Bayesian networks among the errors

Authors: Abdul Salam Marco Grzegorczyk

Source: Computational Statistics. 38:779-808

Subject Terms: Statistics and Probability, Reversible-jump Markov chain Monte Carlo, Artificial intelligence, Learning and Inference in Bayesian Networks, Bayesian inference, Bayesian probability, Generalized Linear Models, Pairwise comparison, Quantum mechanics, 01 natural sciences, Gaussian Bayesian networks, Directed acyclic graph, Multivariate Bayesian linear regression, Methods for Handling Missing Data in Statistical Analysis, Inference, Artificial Intelligence, Conditional independence, 0502 economics and business, FOS: Mathematics, 0101 mathematics, Hidden Markov Models, Covariance, Probabilistic Learning, Physics, Statistics, 05 social sciences, Mixed-Effects Models, Covariate, Structure Learning, Computer science, Approximate inference, Algorithm, Bayesian network, Exact Bayesian model averaging, Computer Science, Physical Sciences, Gaussian, Model-Based Clustering with Mixture Models, Seemingly unrelated regression, Mathematics, Graphical model

Certainty-based Preference Completion

Authors: Lei Li Minghe Xue Zan Zhang et al.

Source: Data Intelligence. 4:112-133

Subject Terms: Artificial intelligence, Learning and Inference in Bayesian Networks, Imprecise Probabilities, Trajectory Data Mining and Analysis, Probabilistic Databases, Social Sciences, Geometry, Group Decision Making, Set (abstract data type), 02 engineering and technology, Multi-Criteria Decision Making, Management Science and Operations Research, Preference, Decision Sciences, FOS: Economics and business, Artificial Intelligence, FOS: Mathematics, 0202 electrical engineering, electronic engineering, information engineering, Econometrics, Mathematical economics, 4. Education, Statistics, Ranking (information retrieval), Certainty, 16. Peace & justice, Computer science, Programming language, Signal Processing, Computer Science, Physical Sciences, Mathematics

Access URL: https://direct.mit.edu/dint/article-pdf/4/1/112/1985064/dint_a_00115.pdf

An Enhanced Memetic Algorithm for Feature Selection in Big Data Analytics with MapReduce

Authors: Nandhagopal Nachimuthu Umanesan Ramakrishnan

Source: Intelligent Automation & Soft Computing. 31:1547-1559

Subject Terms: FOS: Computer and information sciences, Artificial intelligence, Hyperparameter Optimization, Support vector machine, Learning and Inference in Bayesian Networks, Economics, Feature (linguistics), Heuristic, Metaheuristic, Local optimum, 02 engineering and technology, Constraint satisfaction, 01 natural sciences, Learning with Noisy Labels in Machine Learning, Swarm behaviour, Selection (genetic algorithm), Artificial Intelligence, Data Mining Techniques and Applications, Meta-Learning, Machine learning, 0202 electrical engineering, electronic engineering, information engineering, Hybrid algorithm (constraint satisfaction), 14. Life underwater, 0101 mathematics, Data mining, Economic growth, Probabilistic logic, Constraint logic programming, Probabilistic Learning, Memetic algorithm, Linguistics, Inference Algorithms, Computer science, FOS: Philosophy, ethics and religion, Programming language, Automated Machine Learning, Algorithm, Philosophy, Genetic algorithm, Computer Science, Physical Sciences, Feature selection, Convergence (economics), FOS: Languages and literature, SPARK (programming language), Information Systems

Access URL: https://www.techscience.com/iasc/v31n3/44827/pdf

Causal Artificial Intelligence for High-Stakes Decisions: The Design and Development of a Causal Machine Learning Model

Authors: Bukhoree Sahoh Kanjana Haruehansapong Mallika Kliangkhlao

Source: IEEE Access, Vol 10, Pp 24327-24339 (2022)

Subject Terms: Causal Inference, Artificial intelligence, Learning and Inference in Bayesian Networks, Explainable Artificial Intelligence, Handling Imbalanced Data in Classification Problems, Business decision mapping, 02 engineering and technology, Quantum mechanics, Gene, Biochemistry, Causal model, FOS: Economics and business, Causal Discovery, do-calculus, Cognition, Artificial Intelligence, Machine learning, Causal structure, FOS: Mathematics, 0202 electrical engineering, electronic engineering, information engineering, Psychology, Econometrics, causal inference, Decision support system, ENCODE, causal science, Cost-Sensitive Learning, Probabilistic Learning, Evidential reasoning approach, 9. Industry and infrastructure, Physics, 4. Education, Statistics, Responsibility in AI, Computer science, counterfactuals, TK1-9971, FOS: Psychology, Chemistry, cognitive computing, Bayesian network, Computer Science, Physical Sciences, Causal decision theory, Electrical engineering. Electronics. Nuclear engineering, Causal reasoning, Mathematics, Causal inference, Neuroscience

Access URL: https://ieeexplore.ieee.org/ielx7/6287639/6514899/09722845.pdf
https://doaj.org/article/5b4de778a9af47448aa6cc7f729a5797

Independent events and their complements. Part II

Authors: Gabriel Perez Mizuno Carolina Martins Crispim Adrián Pizzinga

Source: International Journal of Mathematical Education in Science and Technology. 53:3470-3483

Subject Terms: Statistics and Probability, Independence (probability theory), Causal Inference, Learning and Inference in Bayesian Networks, Corollary, Epistemology, Quantum mechanics, 01 natural sciences, Artificial Intelligence, Statistical Mechanics with Long-Range Interactions and Nonextensivity, Conditional independence, FOS: Mathematics, 0101 mathematics, Event (particle physics), Extension (predicate logic), Mathematical economics, Physics, Statistics, Statistical Analysis of Benford's Law Phenomenon, Statistical and Nonlinear Physics, Discrete mathematics, Computer science, FOS: Philosophy, ethics and religion, Programming language, Philosophy, Physics and Astronomy, Computer Science, Physical Sciences, Mathematics, Consistency (knowledge bases)

A logical framework to study concept-learning biases in the presence of multiple explanations

Authors: Pablo Tano Santiago Figueira Sergio Abriola et al.

Source: Behav Res Methods
CONICET Digital (CONICET)
Consejo Nacional de Investigaciones Científicas y Técnicas

Subject Terms: Exploit, Artificial intelligence, Counterexample, Learning and Inference in Bayesian Networks, Logic, Concept Formation, Construct (python library), Set (abstract data type), Article, Theoretical computer science, Bias, Artificial Intelligence, Computer security, Machine learning, FOS: Mathematics, Genetics, PROPOSITIONAL LOGIC, Humans, Learning, 0501 psychology and cognitive sciences, Adaptation to Concept Drift in Data Streams, Biology, Ensemble Learning, Probabilistic Learning, Natural language processing, 05 social sciences, Structure Learning, Discrete mathematics, Computer science, CONCEPT LEARNING, Programming language, Automatic Keyword Extraction from Textual Data, FOS: Biological sciences, Computer Science, Physical Sciences, LEARNING BIASES, Mathematics, Sequence (biology)

File Description: application/pdf

Access URL: https://link.springer.com/content/pdf/10.3758/s13428-021-01596-4.pdf
https://pubmed.ncbi.nlm.nih.gov/34145547
https://link.springer.com/content/pdf/10.3758/s13428-021-01596-4.pdf
https://link.springer.com/article/10.3758/s13428-021-01596-4
http://hdl.handle.net/11336/151725

Efficient and Scalable Structure Learning for Bayesian Networks: Algorithms and Applications

Authors: Rong Zhu Andreas Pfadler Zhanjun Wu et al.

Source: 2021 IEEE 37th International Conference on Data Engineering (ICDE). :2613-2624

Subject Terms: FOS: Computer and information sciences, Data Quality Assessment and Improvement, Computer Science - Machine Learning, Artificial intelligence, Learning and Inference in Bayesian Networks, Computer Science - Artificial Intelligence, Social Sciences, Anomaly detection, 02 engineering and technology, Management Science and Operations Research, 01 natural sciences, Learning with Noisy Labels in Machine Learning, Machine Learning (cs.LG), Decision Sciences, Database, Deep Learning, Theoretical computer science, Computer Science - Databases, Artificial Intelligence, Meta-Learning, Machine learning, 0202 electrical engineering, electronic engineering, information engineering, 0101 mathematics, Probabilistic Learning, Scalability, Databases (cs.DB), Structure Learning, Computer science, Distributed computing, Algorithm, Overhead (engineering), Operating system, Artificial Intelligence (cs.AI), Bayesian network, Computer Science, Physical Sciences, Robust Learning

Access URL: http://arxiv.org/pdf/2012.03540
http://arxiv.org/abs/2012.03540
https://ieeexplore.ieee.org/document/9458847
https://dblp.uni-trier.de/db/conf/icde/icde2021.html#ZhuPWHYYQZC21

Consistency of distributed preference relations

Authors: Weiyong Liu Chao Fu Wenjun Chang et al.

Source: Journal of the Operational Research Society. 73:905-918

Subject Terms: Rough Sets Theory and Applications, Artificial intelligence, Learning and Inference in Bayesian Networks, Science Policy, Immunology, 0211 other engineering and technologies, Social Sciences, Three-Way Decisions, Group Decision Making, Set (abstract data type), 02 engineering and technology, Multi-Criteria Decision Making, Management Science and Operations Research, Operations research, Social psychology, Preference, Mathematical Sciences not elsewhere classified, Decision Sciences, Artificial Intelligence, Group decision-making, FOS: Mathematics, 0202 electrical engineering, electronic engineering, information engineering, Preference relation, Psychology, multiple criteria group decision-making, Distributed preference relation, transitivity, Molecular Biology, consistency, Statistics, Computational Biology, Computer science, selection of associate head, Process (computing), Programming language, FOS: Psychology, Operating system, Infectious Diseases, Computational Theory and Mathematics, Combinatorics, Computer Science, Physical Sciences, Medicine, Transitive relation, Consistency (knowledge bases), Mathematics, Biological Sciences not elsewhere classified

Access URL: https://www.tandfonline.com/doi/full/10.1080/01605682.2020.1865849

Elicitation of the Parameters of Multiple Linear Models

Authors: Carlos Barrera-Causil Juan Carlos Correa

Source: Revista Colombiana de Estadística, Volume: 44, Issue: 1, Pages: 159-170, Published: 27 FEB 2021

Subject Terms: Artificial intelligence, Conjugate prior, Learning and Inference in Bayesian Networks, Construct (python library), Linear model, Social Sciences, Expert Judgment, 02 engineering and technology, Management Science and Operations Research, Bayesian statistics, Informative distribution, Nonparametric Methods, Bayesian probability, 01 natural sciences, Elicitación, Decision Sciences, Gaussian Processes in Machine Learning, Variance (accounting), Artificial Intelligence, Prior probability, Accounting, Machine learning, FOS: Mathematics, 0202 electrical engineering, electronic engineering, information engineering, Business, 0101 mathematics, Estadística Bayesiana, Data mining, Probabilistic Models, Probabilistic Graphical Models, Probabilistic Learning, Statistics, Elicitation, Conjugate distribution, Computer science, Distribución informativa, Process (computing), Programming language, Operating system, Computer Science, Physical Sciences, Time Series Forecasting Methods, Distribución conjugada, Mathematics

File Description: text/html

Access URL: https://rcb.unal.edu.co/index.php/estad/article/view/83525
https://revistas.unal.edu.co/index.php/estad/article/view/83525
http://www.scielo.org.co/pdf/rce/v44n1/0120-1751-rce-44-01-159.pdf
http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0120-17512021000100159
http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0120-17512021000100159&lng=en&tlng=en