Hamiltonian Monte Carlo Methods in Machine Learning

Name: Hamiltonian Monte Carlo Methods in Machine Learning
Brand: Elsevier Science Publishing Co Inc
SKU: 9780443190353
Price: 4438 CZK
Availability: InStock

Autor: Rendani Mbuvha, Wilson Tsalane Mongwe

Jazyk:

Vazba: Brožovaná

Vydavatel: Elsevier Science Publishing Co Inc

Dostupnost: Skladem u dodavatele

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4 438 Kč

Hamiltonian Monte Carlo Methods in Machine Learning introduces methods for optimal tuning of HMC par...

Informace o knize

Autor

Rendani Mbuvha, Wilson Tsalane Mongwe

Jazyk

Angličtina

Vazba

Kniha - Brožovaná

Vydáno

2023

Stránek

300

EAN

9780443190353

ISBN

0443190356

Enbook ID

39514269

Vydavatel

Elsevier Science Publishing Co Inc

Hmotnost

390

Rozměry

191 x 235

Kompletní popis

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

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