Hamiltonian Monte Carlo Methods in Machine Learning

This book PDF is perfect for those who love Computers genre, written by Tshilidzi Marwala and published by Elsevier which was released on 03 February 2023 with total hardcover pages 222. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Hamiltonian Monte Carlo Methods in Machine Learning books below.

Hamiltonian Monte Carlo Methods in Machine Learning
Author : Tshilidzi Marwala
File Size : 45,8 Mb
Publisher : Elsevier
Language : English
Release Date : 03 February 2023
ISBN : 9780443190360
Pages : 222 pages
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Hamiltonian Monte Carlo Methods in Machine Learning by Tshilidzi Marwala Book PDF Summary

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

Hamiltonian Monte Carlo Methods in Machine Learning

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

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