Probability and Stochastic Processes : A Friendly Introduction for Electrical and Computer Engineers

By:

Yates, Roy D [author]

Contributor(s):

Goodman, David J [co-author]

Material type:

TextLanguage: English Publication details: New Delhi : Wiley , 2005 .Edition: 2ndDescription: xvii,519pISBN:

9788126534319

Subject(s):

Other classification:

B281 P5 TB

Summary: This user-friendly resource helps readers grasp the concepts of probability and stochastic processes, so they can apply them in professional engineering practice. The book presents concepts clearly as a sequence of building blocks that are identified either as an axiom, definition, or theorem. This approach provides a better understanding of the material, which can be used to solve practical problems. • Experiments, Models, and Probabilities. • Discrete Random Variables. • Continuous Random Variables. • Pairs of Random Variables. • Random Vectors. • Sums of Random Variables. • Parameter Estimation Using the Sample Mean. • Hypothesis Testing. • Estimation of a Random Variable. • Stochastic Processes. • Random Signal Processing. • Markov Chains.

Item type:

Textbook

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Holdings
Cover image	Item type	Current library	Home library	Collection	Shelving location	Call number	Materials specified	Vol info	URL	Copy number	Status	Notes	Date due	Barcode	Item holds	Item hold queue priority	Course reserves
	Textbook	Central Science Library	Central Science Library			B281 P5 TB (Browse shelf(Opens below))					Available			SL1558514

Included Appendix A & B 501-510p.; References 511-512p.; Index 513-519p.

This user-friendly resource helps readers grasp the concepts of probability and stochastic processes, so they can apply them in professional engineering practice. The book presents concepts clearly as a sequence of building blocks that are identified either as an axiom, definition, or theorem. This approach provides a better understanding of the material, which can be used to solve practical problems. • Experiments, Models, and Probabilities. • Discrete Random Variables. • Continuous Random Variables. • Pairs of Random Variables. • Random Vectors. • Sums of Random Variables. • Parameter Estimation Using the Sample Mean. • Hypothesis Testing. • Estimation of a Random Variable. • Stochastic Processes. • Random Signal Processing. • Markov Chains.

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