Gregory K. Miller - Böcker
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2 produkter
2 produkter
Del 378 - Wiley Series in Probability and Statistics
Elements of Applied Stochastic Processes
Inbunden, Engelska, 2002
2 078 kr
Skickas inom 7-10 vardagar
This 3rd edition of the successful Elements of Applied Stochastic Processes improves on the last edition by condensing the material and organising it into a more teachable format. It provides more in-depth coverage of Markov chains and simple Markov process and gives added emphasis to statistical inference in stochastic processes. Integration of theory and application offers improved teachability Provides a comprehensive introduction to stationary processes and time series analysis Integrates a broad set of applications into the text Utilizes a wealth of examples from research papers and monographs
2 270 kr
Skickas inom 7-10 vardagar
Improve Your Probability of Mastering This Topic This book takes an innovative approach to calculus-based probability theory, considering it within a framework for creating models of random phenomena. The author focuses on the synthesis of stochastic models concurrent with the development of distribution theory while also introducing the reader to basic statistical inference. In this way, the major stochastic processes are blended with coverage of probability laws, random variables, and distribution theory, equipping the reader to be a true problem solver and critical thinker. Deliberately conversational in tone, Probability is written for students in junior- or senior-level probability courses majoring in mathematics, statistics, computer science, or engineering. The book offers a lucid and mathematicallysound introduction to how probability is used to model random behavior in the natural world. The text contains the following chapters:* Modeling* Sets and Functions* Probability Laws I: Building on the Axioms* Probability Laws II: Results of Conditioning* Random Variables and Stochastic Processes* Discrete Random Variables and Applications in Stochastic Processes* Continuous Random Variables and Applications in Stochastic Processes* Covariance and Correlation Among Random Variables Included exercises cover a wealth of additional concepts, such as conditional independence, Simpson's paradox, acceptance sampling, geometric probability, simulation, exponential families of distributions, Jensen's inequality, and many non-standard probability distributions.