Hamiltonian Monte Carlo Methods in Machine Learning
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- Taschenbuch
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Form:Einzelkauf Download
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Sprache:Englisch
160,00 €
inkl. gesetzl. MwSt.Beschreibung
Produktdetails
Format
ePUB 3
Kopierschutz
Nein
Family Sharing
Nein
Text-to-Speech
Ja
Erscheinungsdatum
03.02.2023
Verlag
Elsevier Science & Techn.Seitenzahl
220 (Printausgabe)
Dateigröße
31287 KB
Sprache
Englisch
EAN
9780443190360
Hamiltonian Monte Carlo Methods in Machine Learning introduces methods for optimal tuning of HMC parameters, along with an introduction of Shadow and Non-canonical HMC methods with improvements and speedup. Lastly, the authors address the critical issues of variance reduction for parameter estimates of numerous HMC based samplers. The book offers a comprehensive introduction to Hamiltonian Monte Carlo methods and provides a cutting-edge exposition of the current pathologies of HMC-based methods in both tuning, scaling and sampling complex real-world posteriors. These are mainly in the scaling of inference (e.g., Deep Neural Networks), tuning of performance-sensitive sampling parameters and high sample autocorrelation.
Other sections provide numerous solutions to potential pitfalls, presenting advanced HMC methods with applications in renewable energy, finance and image classification for biomedical applications. Readers will get acquainted with both HMC sampling theory and algorithm implementation.
- Provides in-depth analysis for conducting optimal tuning of Hamiltonian Monte Carlo (HMC) parameters
- Presents readers with an introduction and improvements on Shadow HMC methods as well as non-canonical HMC methods
- Demonstrates how to perform variance reduction for numerous HMC-based samplers
- Includes source code from applications and algorithms
Other sections provide numerous solutions to potential pitfalls, presenting advanced HMC methods with applications in renewable energy, finance and image classification for biomedical applications. Readers will get acquainted with both HMC sampling theory and algorithm implementation.
- Provides in-depth analysis for conducting optimal tuning of Hamiltonian Monte Carlo (HMC) parameters
- Presents readers with an introduction and improvements on Shadow HMC methods as well as non-canonical HMC methods
- Demonstrates how to perform variance reduction for numerous HMC-based samplers
- Includes source code from applications and algorithms
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