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The book moves from probability, Monte Carlo methods, model calibration, and Bayesian inference to supervised learning, unsupervised learning, state-space models, Kalman filtering, probabilistic programming, and automated Bayesian inference. Physics-informed machine learning is integrated throughout, emphasizing the combination of data and physical insight.
Implemented entirely in reproducible Jupyter notebooks using Python, the text helps readers understand the foundations of modern methods, build models that reflect physical and causal structure, and calibrate them rigorously with data.
The book moves from probability, Monte Carlo methods, model calibration, and Bayesian inference to supervised learning, unsupervised learning, state-space models, Kalman filtering, probabilistic programming, and automated Bayesian inference. Physics-informed machine learning is integrated throughout, emphasizing the combination of data and physical insight.
Implemented entirely in reproducible Jupyter notebooks using Python, the text helps readers understand the foundations of modern methods, build models that reflect physical and causal structure, and calibrate them rigorously with data.
| Erscheinungsjahr: | 2026 |
|---|---|
| Fachbereich: | Datenkommunikation, Netze & Mailboxen |
| Genre: | Importe, Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| ISBN-13: | 9789819842919 |
| ISBN-10: | 9819842913 |
| Sprache: | Englisch |
| Einband: | Kartoniert / Broschiert |
| Autor: | Bilionis Ilias |
| Hersteller: | World Scientific |
| Verantwortliche Person für die EU: | Libri GmbH, Europaallee 1, D-36244 Bad Hersfeld, gpsr@libri.de |
| Maße: | 229 x 152 x 38 mm |
| Von/Mit: | Bilionis Ilias |
| Erscheinungsdatum: | 11.10.2026 |
| Gewicht: | 1,241 kg |
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