Übersicht			Auf mobile öffnen

Lieferstatus:	Auf Bestellung (Lieferzeit unbekannt)
Veröffentlichung:	Februar 2020
Genre:	Schulbücher
	B / Computational Mathematics and Numerical Analysis / Computer Imaging, Vision, Pattern Recognition and Graphics / Computer mathematics / Computer Vision / Mathematical & statistical software / Mathematical statistics / Mathematics and Statistics / Maths for computer scientists / Optical data processing / Probability & statistics / Probability and Statistics in Computer Science / Statistical Theory and Methods / Statistics
ISBN:	9789811329708
EAN-Code:	9789811329708
Verlag:	Springer Nature EN
Einband:	Gebunden
Sprache:	English
Dimensionen:	H 240 mm / B 168 mm / D
Gewicht:	1036 gr
Seiten:	422
Illustration:	XVI, 422 p. 250 illus., 185 illus. in color., schwarz-weiss Illustrationen, farbige Illustrationen
Bewertung:	Titel bewerten / Meinung schreiben

Inhalt:

This book seeks to bridge the gap between statistics and computer science. It provides an overview of Monte Carlo methods, including Sequential Monte Carlo, Markov Chain Monte Carlo, Metropolis-Hastings, Gibbs Sampler, Cluster Sampling, Data Driven MCMC, Stochastic Gradient descent, Langevin Monte Carlo, Hamiltonian Monte Carlo, and energy landscape mapping. Due to its comprehensive nature, the book is suitable for developing and teaching graduate courses on Monte Carlo methods. To facilitate learning, each chapter includes several representative application examples from various fields. The book pursues two main goals: (1) It introduces researchers to applying Monte Carlo methods to broader problems in areas such as Computer Vision, Computer Graphics, Machine Learning, Robotics, Artificial Intelligence, etc.; and (2) it makes it easier for scientists and engineers working in these areas to employ Monte Carlo methods to enhance their research.