000			01880nam a22003255i 4500
001			91161
005			20231026103721.0
010			_a978-981-13-2971-5 _dcompra
090			_a91161
100			_a20231023d2020 k||y0pory50 ba
101	0		_aeng
102			_aSG
200	1		_aMonte carlo methods _bDocumento eletrónico _fby Adrian Barbu, Song-Chun Zhu
210			_aSingapore _cSpringer Nature Singapore _cSpringer _d2020
215			_aXVI, 422 p. _cil.
303			_aThis 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.
606			_aMathematics _xData processing
606			_aComputer science _xMathematics
606			_aMathematical statistics
606			_aImage processing _xDigital techniques
606			_aComputer vision
606			_aStatistics
680			_aQA71-90
700	1		_aBarbu _bAdrian
701	1		_aZhu _bSong-Chun
801		0	_aPT _gRPC
856	4		_uhttps://doi.org/10.1007/978-981-13-2971-5
942			_2lcc _cF _n0