Applied probability /

Applied probability /

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"This textbook on applied probability is intended for graduate students in applied mathematics, biostatistics, computational biology, computer science, physics, and statistics. It presupposes knowledge of multivariate calculus, linear algebra, ordinary differential equations, and elementary pro...

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Bibliographic Details
Main Author: Lange, Kenneth
Format: Book
Language:English
Published: New York : Springer, ©2003.
Series:Springer texts in statistics.
Subjects:
Probabilities.
Stochastic processes.
Probability
Stochastic Processes
Probabilités.
Processus stochastiques.
probability.
Waarschijnlijkheidstheorie.
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245 1 0 |a Applied probability /  |c Kenneth Lange. 
260 |a New York :  |b Springer,  |c ©2003. 
300 |a xii, 300 pages :  |b illustrations ;  |c 24 cm. 
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504 |a Includes bibliographical references (pages 285-294) and index. 
505 0 0 |t Basic Notions of Probability Theory --  |t Probability and Expectation --  |t Conditional Probability --  |t Independence --  |t Distributions, Densities, and Moments --  |t Convolution --  |t Random Vectors --  |t Multivariate Normal Random Vectors --  |t Convergence --  |t Calculation of Expectations --  |t Indicator Random Variables and Symmetry --  |t Conditioning --  |t Moment Transforms --  |t Tail Probability Methods --  |t Moments of Reciprocals and Ratios --  |t Reduction of Degree --  |t Spherical Surface Measure --  |t Dirichlet Distribution --  |t Convexity, Optimization, and Inequalities --  |t Convex Functions --  |t Minimization of Convex Functions --  |t The MM Algorithm --  |t Moment Inequalities --  |t Combinatorics --  |t Inclusion-Exclusion --  |t Applications to Order Statistics --  |t Stirling Numbers --  |t Application to an Urn Model --  |t Pigeonhole Principle --  |t Combinatorial Optimization --  |t Quick Sort --  |t Data Compression and Huffman Coding --  |t Graph Coloring --  |t Point Sets with Only Acute Angles --  |t Sperner's Theorem --  |t Subadditivity and Expectations --  |t Poisson Processes --  |t The Poisson Distribution --  |t Characterization and Construction --  |t One-Dimensional Processes --  |t Transmission Tomography --  |t Mathematical Applications --  |t Transformations --  |t Marking and Coloring --  |t Campbell's Moment Formulas --  |t Discrete-Time Markov Chains --  |t Definitions and Elementary Theory --  |t Coupling --  |t Hitting Probabilities and Hitting Times --  |t Markov Chain Monte Carlo --  |t The Hastings-Metropolis Algorithm --  |t Gibbs Sampling --  |t Convergence of the Independence Sampler --  |t Simulated Annealing --  |t Continuous-Time Markov Chains. 
520 1 |a "This textbook on applied probability is intended for graduate students in applied mathematics, biostatistics, computational biology, computer science, physics, and statistics. It presupposes knowledge of multivariate calculus, linear algebra, ordinary differential equations, and elementary probability theory. Given these prerequisites, Applied Probability presents a unique blend of theory and applications, with special emphasis on mathematical modeling, computational techniques, and examples from the biological sciences."--Jacket. 
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650 0 |a Stochastic processes. 
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