1. An overview of random variables and probability distributions
2. Introduction to random processes
3. Stationarity of random processes
4. Autocorrelation and its properties
5. Binomial and poisson processes
6. Normal process (gaussian process)
7. Spectrum estimation: ergodicity
8. Power spectrum: power spectral density functions
9. Markov process and markov chain.

• Nine well organized chapters
• Full coverage of important probability concepts and distributions
• Full coverage of multivariate normal distribution
• Full coverage of concepts of random processes
• Full coverage of stationarity of random processes
• Full coverage of autocorrelation and its properties
• Full coverage of standard distribution-based special processes
• Full coverage Markov process and Marko chains
• Explanation with suitable examples and graphical representations
• Solved problems with illustrations for easy understanding
• More than 200 problems in total
• 100 solved examples
• 100-exercise problems with answers
• A number of graphs