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010   $a981-13-2971-0
024 7 $a10.1007/978-981-13-2971-5
035   $a(CKB)4100000010480199
035   $a(DE-He213)978-981-13-2971-5
035   $a(MiAaPQ)EBC6122025
035   $a(PPN)24297760X
035   $a(EXLCZ)994100000010480199
100   $a20200224d2020      u|         0
101 0 $aeng
135   $aurnn|008mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aMonte Carlo Methods /$fby Adrian Barbu, Song-Chun Zhu
205   $a1st ed. 2020.
210  1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2020.
215   $a1 online resource (XVI, 422 p. 250 illus., 185 illus. in color.) 
311 08$a981-13-2970-2 
320   $aIncludes bibliographical references and index.
327   $a1 Introduction to Monte Carlo Methods -- 2 Sequential Monte Carlo -- 3 Markov Chain Monte Carlo - the Basics -- 4 Metropolis Methods and Variants -- 5 Gibbs Sampler and its Variants -- 6 Cluster Sampling Methods -- 7 Convergence Analysis of MCMC -- 8 Data Driven Markov Chain Monte Carlo -- 9 Hamiltonian and Langevin Monte Carlo -- 10 Learning with Stochastic Gradient -- 11 Mapping the Energy Landscape.
330   $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
606   $aStatistics
606   $aComputational Mathematics and Numerical Analysis
606   $aProbability and Statistics in Computer Science
606   $aComputer Imaging, Vision, Pattern Recognition and Graphics
606   $aStatistical Theory and Methods
606   $aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences
615  0$aMathematics$xData processing.
615  0$aComputer science$xMathematics.
615  0$aMathematical statistics.
615  0$aImage processing$xDigital techniques.
615  0$aComputer vision.
615  0$aStatistics.
615  0$aStatistics.
615 14$aComputational Mathematics and Numerical Analysis.
615 24$aProbability and Statistics in Computer Science.
615 24$aComputer Imaging, Vision, Pattern Recognition and Graphics.
615 24$aStatistical Theory and Methods.
615 24$aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
676   $a519.282
700   $aBarbu$b Adrian$4aut$4http://id.loc.gov/vocabulary/relators/aut$01002774
702   $aZhu$b Song-Chun$4aut$4http://id.loc.gov/vocabulary/relators/aut
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910483831603321
996   $aMonte Carlo Methods$92301695
997   $aUNINA