Probabilistic systems and random signals

Author: Abraham H. Haddad
Publisher: Prentice Hall
ISBN:
Format: PDF, ePub, Mobi
Download Now
In-depth mathematical treatment, including examples of real systems to explain many of the probabilistic models and the use of Matlab both in examples and problem assignments, ensures students can relate to the mathematical material in practical terms Unique applications--covering issues such as reliability, measurement errors, and arrival and departure of events in networks--provide students with a broader range of topical coverage.

Probabilistic Methods of Signal and System Analysis

Author: George R. Cooper
Publisher: Oxford University Press, USA
ISBN: 9780195123548
Format: PDF, ePub, Mobi
Download Now
This text provides an introduction to the applications of probability theory to the solution of problems arising in the analysis of signals and systems. Since its original publication in 1971, this text has been a standard for signals and systems courses that emphasise probability. The new edition incorporates a much greater use of the computer in examples and problems. It increases the number and variety of examples, such as estimating the parameters of random processes and processing them through linear systems. In this edition, the use of the computer is introduced both in text examples and in selected problems. The computer examples are carried out using MATLAB. A number of new sections have been added relating to Benoulli trials, correlation of data sets, smoothing of data, computer computation of correlation functions, and spectral densities and system simulation. Key Features:* Stresses engineering applications of probability theory* Presents the material at a level and in a manner appropriate for engineering majors, as opposed to mathematics majorsSupplement:Solutions Manual: 0195123557Contents:PrefaceIntroduction to ProbabilityRandom VariablesSeveral Random VariablesElements of StatisticsRandom ProcessesCorrelation FunctionsSpectral DensityRepines of Linear Systems to Random InputsOptimum Linear SystemsAppendices: Mathematical TablesFrequently Encountered Probability DistributionsBinomial CoefficientsNormal Probability Distribution FunctionThe Q-FunctionStudent's T-Distribution FunctionComputer ComputationsTable of Correlation Function-Spectral Density PairsContour Integration

Probability Random Signals and Statistics

Author: X. Rong Li
Publisher: CRC Press
ISBN: 9780849304330
Format: PDF, Mobi
Download Now
With this innovative text, the study-and teaching- of probability and random signals becomes simpler, more streamlined, and more effective. Its unique "textgraph" format makes it both student-friendly and instructor-friendly. Pages with a larger typeface form a concise text for basic topics and make ideal transparencies; pages with smaller type provide more detailed explanations and more advanced material.

Probability Statistics and Random Signals

Author: Charles G. Boncelet
Publisher: Oxford University Press, USA
ISBN: 9780190200510
Format: PDF, Mobi
Download Now
Probability, Statistics, and Random Signals offers a comprehensive treatment of probability, giving equal treatment to discrete and continuous probability. The topic of statistics is presented as the application of probability to data analysis, not as a cookbook of statistical recipes. This student-friendly text features accessible descriptions and highly engaging exercises on topics like gambling, the birthday paradox, and financial decision-making.

Random Signals

Author: K. Sam Shanmugan
Publisher: Wiley
ISBN: 9780471815556
Format: PDF
Download Now
Random Signals, Noise and Filtering develops the theory of random processes and its application to the study of systems and analysis of random data. The text covers three important areas: (1) fundamentals and examples of random process models, (2) applications of probabilistic models: signal detection, and filtering, and (3) statistical estimation--measurement and analysis of random data to determine the structure and parameter values of probabilistic models. This volume by Breipohl and Shanmugan offers the only one-volume treatment of the fundamentals of random process models, their applications, and data analysis.

Probability Random Processes and Statistical Analysis

Author: Hisashi Kobayashi
Publisher: Cambridge University Press
ISBN: 1139502611
Format: PDF
Download Now
Together with the fundamentals of probability, random processes and statistical analysis, this insightful book also presents a broad range of advanced topics and applications. There is extensive coverage of Bayesian vs. frequentist statistics, time series and spectral representation, inequalities, bound and approximation, maximum-likelihood estimation and the expectation-maximization (EM) algorithm, geometric Brownian motion and Itô process. Applications such as hidden Markov models (HMM), the Viterbi, BCJR, and Baum–Welch algorithms, algorithms for machine learning, Wiener and Kalman filters, and queueing and loss networks are treated in detail. The book will be useful to students and researchers in such areas as communications, signal processing, networks, machine learning, bioinformatics, econometrics and mathematical finance. With a solutions manual, lecture slides, supplementary materials and MATLAB programs all available online, it is ideal for classroom teaching as well as a valuable reference for professionals.

Probability and Random Processes

Author: Venkatarama Krishnan
Publisher: John Wiley & Sons
ISBN: 1119011914
Format: PDF, ePub, Mobi
Download Now
The second edition enhanced with new chapters, figures, and appendices to cover the new developments in applied mathematical functions This book examines the topics of applied mathematical functions to problems that engineers and researchers solve daily in the course of their work. The text covers set theory, combinatorics, random variables, discrete and continuous probability, distribution functions, convergence of random variables, computer generation of random variates, random processes and stationarity concepts with associated autocovariance and cross covariance functions, estimation theory and Wiener and Kalman filtering ending with two applications of probabilistic methods. Probability tables with nine decimal place accuracy and graphical Fourier transform tables are included for quick reference. The author facilitates understanding of probability concepts for both students and practitioners by presenting over 450 carefully detailed figures and illustrations, and over 350 examples with every step explained clearly and some with multiple solutions. Additional features of the second edition of Probability and Random Processes are: Updated chapters with new sections on Newton-Pepys’ problem; Pearson, Spearman, and Kendal correlation coefficients; adaptive estimation techniques; birth and death processes; and renewal processes with generalizations A new chapter on Probability Modeling in Teletraffic Engineering written by Kavitha Chandra An eighth appendix examining the computation of the roots of discrete probability-generating functions With new material on theory and applications of probability, Probability and Random Processes, Second Edition is a thorough and comprehensive reference for commonly occurring problems in probabilistic methods and their applications.

Random Signals and Processes Primer with MATLAB

Author: Gordana Jovanovic Dolecek
Publisher: Springer Science & Business Media
ISBN: 1461423864
Format: PDF, ePub
Download Now
This book provides anyone needing a primer on random signals and processes with a highly accessible introduction to these topics. It assumes a minimal amount of mathematical background and focuses on concepts, related terms and interesting applications to a variety of fields. All of this is motivated by numerous examples implemented with MATLAB, as well as a variety of exercises at the end of each chapter.

High Dimensional Probability

Author: Roman Vershynin
Publisher: Cambridge University Press
ISBN: 1108415199
Format: PDF, Kindle
Download Now
An integrated package of powerful probabilistic tools and key applications in modern mathematical data science.