Probability and Random Processes for Electrical and Computer Engineers
Bernoulli random variables 5. Geometric random variables 5. Exponential random variables 5. Gaussian random variables 5. Squared Gaussian random variables 5. Summary 6. Inequalities, Limit Theorems, and Parameter Estimation 6. Inequalities 6. Markov inequality 6. Chebyshev inequality 6.
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One-sided Chebyshev inequality 6. Other inequalities 6. Convergence and Limit Theorems 6. Laws of large numbers 6. Central limit theorem 6. Estimation of Parameters 6. Estimates and properties 6. Sample mean and variance 6. Maximum Likelihood Estimation 6. Point Estimates and Confidence Intervals 6. Application to Signal Estimation 6.
Estimating a signal in noise 6. Choosing the number of samples 6. Applying confidence intervals 6. Summary 7. Random Vectors 7. Cumulative distribution and density functions 7. Random vectors with independent components 7. Analysis of Random Vectors 7. Expectation and moments 7. Estimating moments from data 7. Random vectors with uncorrelated components 7.
Probability and Random Processes for Electrical and Computer Engineers (豆瓣)
Transformations 7. Transformation of moments 7. Transformation of density functions 7. Cross Correlation and Covariance 7. Applications to Signal Processing 7. Digital communication 7. Pattern recognition 7. Vector quantization 7. Summary pt. II Introduction to Random Processes 8. Random Processes 8. Introduction 8. Some types of random processes 8. Signals as random processes 8.
Continuous versus discrete 8. Characterizing a Random Process 8. Time averages vs.
Regular and predictable random processes 8. Periodic random processes 8.
Probability and Random Processes
Some Discrete Random Processes 8. Bernoulli process 8. Random walk 8. IID random process 8. Markov process discrete 8. Some Continuous Random Processes 8. Wiener process 8. Gaussian white noise 8. Other Gaussian processes 8.
Poisson process 8. Markov chain continuous 8. Summary 9. Random Signals in the Time Domain 9. First and Second Moments of a Random Process 9. Mean and variance 9. Autocorrelation and autocovariance functions 9. Wide sense stationarity 9. Properties of autocorrelation functions 9. Cross Correlation 9. Complex Random Processes 9. Autocorrelation for complex processes 9. Cross-correlation, covariance and properties 9.
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Discrete Random Processes 9. Transformation by Linear Systems 9. Continuous signals and systems 9. Introduction to random processes.
The Poisson process 10 Introduction to random processes. Advanced concepts in random processes. Introduction to Markov chains. Continuoustime Markov chains. Mean convergence and applications. Bivariate random variables.
Probability, Statistics, and Random Processes For Electrical Engineering, 3rd edition
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