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Bimonthly Since 1986 |
ISSN 1004-9037
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Publication Details |
Edited by: Editorial Board of Journal of Data Acquisition and Processing
P.O. Box 2704, Beijing 100190, P.R. China
Sponsored by: Institute of Computing Technology, CAS & China Computer Federation
Undertaken by: Institute of Computing Technology, CAS
Published by: SCIENCE PRESS, BEIJING, CHINA
Distributed by:
China: All Local Post Offices
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09 May 2023, Volume 38 Issue 3
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Abstract
Probabilistic modelling provides a frame-work for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, plays a central role in scientific data analysis, machine learning, robotics, cognitive science, and artificial intelligence. This article provides an introduction to this probabilistic framework, and reviews some state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimisation, data compression, and automatic model discovery.
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