Probabilistic graphical models. Principles and applications

DOI10.1007/978-3-030-61943-5zbMath1453.68001OpenAlexW4255453189MaRDI QIDQ5919036

Luis Enrique Sucar

Publication date: 2 December 2020

Published in: Advances in Computer Vision and Pattern Recognition (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-030-61943-5

Mathematics Subject Classification ID

Software, source code, etc. for problems pertaining to statistics (62-04) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Artificial neural networks and deep learning (68T07) Bayesian inference (62F15) Introductory exposition (textbooks, tutorial papers, etc.) pertaining to computer science (68-01) Introductory exposition (textbooks, tutorial papers, etc.) pertaining to statistics (62-01) Learning and adaptive systems in artificial intelligence (68T05) Applications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.) (60J20) Reasoning under uncertainty in the context of artificial intelligence (68T37) Markov and semi-Markov decision processes (90C40) Software, source code, etc. for problems pertaining to computer science (68-04) Probabilistic graphical models (62H22)

Related Items (2)

PGM_PyLib ⋮ The extended recursive noisy or model: static and dynamic considerations

Uses Software

• NumPy
• Python

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