000			04905nam a22005655i 4500
001			978-1-4419-7165-4
003			DE-He213
005			20140220084511.0
007			cr nn 008mamaa
008			100825s2010 xxu| s |||| 0|eng d
020			_a9781441971654 _9978-1-4419-7165-4
024	7		_a10.1007/978-1-4419-7165-4 _2doi
050		4	_aQA276-280
072		7	_aPBT _2bicssc
072		7	_aMAT029000 _2bisacsh
082	0	4	_a519.5 _223
100	1		_aLange, Kenneth. _eauthor.
245	1	0	_aApplied Probability _h[electronic resource] / _cby Kenneth Lange.
250			_aSecond.
264		1	_aNew York, NY : _bSpringer New York, _c2010.
300			_aXVI, 436 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aSpringer Texts in Statistics, _x1431-875X ; _v0
505	0		_aBasic Notions of Probability Theory -- Calculation of Expectations -- Convexity, Optimization, and Inequalities -- Combinatorics -- Combinatorial Optimization -- Poisson Processes -- Discrete-Time Markov Chains -- Continuous-Time Markov Chains -- Branching Processes -- Martingales -- Diffusion Processes -- Asymptotic Methods -- Numerical Methods -- Poisson Approximation -- Number Theory -- Appendix: Mathematical Review.
520			_aApplied Probability presents a unique blend of theory and applications, with special emphasis on mathematical modeling, computational techniques, and examples from the biological sciences. It can serve as a textbook for graduate students in applied mathematics, biostatistics, computational biology, computer science, physics, and statistics. Readers should have a working knowledge of multivariate calculus, linear algebra, ordinary differential equations, and elementary probability theory. Chapter 1 reviews elementary probability and provides a brief survey of relevant results from measure theory. Chapter 2 is an extended essay on calculating expectations. Chapter 3 deals with probabilistic applications of convexity, inequalities, and optimization theory. Chapters 4 and 5 touch on combinatorics and combinatorial optimization. Chapters 6 through 11 present core material on stochastic processes. If supplemented with appropriate sections from Chapters 1 and 2, there is sufficient material for a traditional semester-long course in stochastic processes covering the basics of Poisson processes, Markov chains, branching processes, martingales, and diffusion processes. The second edition adds two new chapters on asymptotic and numerical methods and an appendix that separates some of the more delicate mathematical theory from the steady flow of examples in the main text. Besides the two new chapters, the second edition includes a more extensive list of exercises, many additions to the exposition of combinatorics, new material on rates of convergence to equilibrium in reversible Markov chains, a discussion of basic reproduction numbers in population modeling, and better coverage of Brownian motion. Because many chapters are nearly self-contained, mathematical scientists from a variety of backgrounds will find Applied Probability useful as a reference. Kenneth Lange is the Rosenfeld Professor of Computational Genetics in the Departments of Biomathematics and Human Genetics at the UCLA School of Medicine and the Chair of the Department of Human Genetics. His research interests include human genetics, population modeling, biomedical imaging, computational statistics, high-dimensional optimization, and applied stochastic processes. Springer previously published his books Mathematical and Statistical Methods for Genetic Analysis, 2nd ed., Numerical Analysis for Statisticians, 2nd ed., and Optimization. He has written over 200 research papers and produced with his UCLA colleague Eric Sobel the computer program Mendel, widely used in statistical genetics.
650		0	_aStatistics.
650		0	_aComputer science.
650		0	_aComputer simulation.
650		0	_aBiology _xMathematics.
650		0	_aComputer science _xMathematics.
650		0	_aDistribution (Probability theory).
650		0	_aMathematical statistics.
650	1	4	_aStatistics.
650	2	4	_aStatistical Theory and Methods.
650	2	4	_aProbability Theory and Stochastic Processes.
650	2	4	_aProbability and Statistics in Computer Science.
650	2	4	_aMathematical Biology in General.
650	2	4	_aComputational Mathematics and Numerical Analysis.
650	2	4	_aSimulation and Modeling.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9781441971647
830		0	_aSpringer Texts in Statistics, _x1431-875X ; _v0
856	4	0	_uhttp://dx.doi.org/10.1007/978-1-4419-7165-4
912			_aZDB-2-SMA
999			_c110727 _d110727