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Verlag GmbH pattern recognition and machine learning
Pattern Recognition And Machine Learning, supplied by Verlag GmbH, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/pattern+recognition+and+machine+learning/pattern+recognition+and+machine+learning/pm40621735-453-1-13
Average 90 stars, based on 1 article reviews
pattern recognition and machine learning - by Bioz Stars, 2026-09
90/100 stars

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Article Title: The Mean-Field Ensemble Kalman Filter: Near-Gaussian Setting
Article Snippet: [5] C. M. Bishop, Pattern Recognition and Machine Learning, Information Science and Statistics, Springer-Verlag, Berlin, 2006, https://doi.org/10.1007/978-0-387-45528-0.

Article Title: Cross validation in stochastic analytic continuation
Article Snippet: [22] C. M. Bishop, Pattern Recognition and Machine Learning, Information Science and Statistics (Springer-Verlag, Berlin, 2006).

Article Title: Tutorial on Molecular Latent Space Simulators (LSSs): Spatially and Temporally Continuous Data-Driven Surrogate Dynamical Models of Molecular Systems.
Article Snippet: The inherently serial nature and requirement for short integration time steps in the numerical integration of molecular dynamics (MD) calculations place strong limitations on the accessible simulation time scales and statistical uncertainties in sampling slowly relaxing dynamical modes and rare events.. Molecular latent space simulators (LSSs) are a data-driven approach to learning a surrogate dynamical model of the molecular system from modest MD training trajectories that can generate synthetic trajectories at a fraction of the computational cost.. The training data may comprise single long trajectories or multiple short, discontinuous trajectories collected over, for example, distributed computing resources.

Article Title: Shape matters: inferring the motility of confluent cells from static images.
Article Snippet: 78 C. M. Bishop, Pattern Recognition and Machine Learning (Information Science and Statistics), Springer-Verlag, Berlin, Heidelberg, 2006.

Article Title: Encoding of body state in whisker-related somatosensory cortex of freely moving mice.
Article Snippet: Pattern Recognition and Machine Learning (Springer- Verlag).

Article Title: Predicting mortality after transcatheter aortic valve replacement using preprocedural CT
Article Snippet: Bishop, C. M. Pattern Recognition and Machine Learning (Springer-Verlag, Berlin, Heidelberg, 2006).

Article Title: Transfer Learning Facilitates the Prediction of Polymer-Surface Adhesion Strength.
Article Snippet: ACS Applied Materials & Interfaces 2022, 14, 37161−37169. (20) Bishop, C. M. Pattern Recognition and Machine Learning (Information Science and Statistics); Springer-Verlag: Berlin, Heidelberg, 2006. (21) Murphy, K. P. Machine learning: a probabilistic perspective; MIT Press: 2012. (22) Murphy, K. P. Probabilistic Machine Learning: An introduction; MIT Press: 2022. (23) Murphy, K. P. Probabilistic Machine Learning: Advanced Topics; MIT Press: 2023. (24) Artrith, N.; Butler, K. T.; Coudert, F.-X.

Article Title: Applying Gaussian Process Machine Learning and Modern Probabilistic Programming to Satellite Data to Infer CO 2 Emissions.
Article Snippet: 2020, 47 (22), No. e2020GL090037. (13) Bishop, C. M. Pattern Recognition and Machine Learning (Information Science and Statistics); Springer-Verlag, 2006. (14) Rasmussen, C. E.; Williams, C. K. I. Gaussian Processes for Machine Learning; MIT Press, 2005. (15) Murphy, K. P. Probabilistic Machine Learning: An Introduction; MIT Press, 2022. (16) Bishop, C. M.; Bishop, H. Deep Learning: Foundations and Concepts; Springer Nature, 2023. (17) Titsias, M. Variational Learning of Inducing Variables in Sparse Gaussian Processes.



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