Need help in Random Processes

Hi
I want help in Random Processes. The project is time bound. You need to solve 3 problems in under 3 hours. Date is 17 Jan. We can discuss further if you are interested.
The syllabus of the course is:
 Random Vectors
o Bivariate random variables: joint, conditional, and marginal distribution
o Random vectors
o Gaussian random vectors
 Estimation
o Optimal estimation, error criteria
o Minimum mean square error (MMSE) estimation
o Linear MMSE (LMMSE) estimation
 Random Processes
o Introduction and basic definitions
o Generating a random process (random parameter, i.i.d. process, recursive definition)
o Joint distribution, autocorrelation function
o Strict-sense and wide-sense stationery (SSS and WSS, respectively) processes
o Gaussian processes
o Examples: moving-average (MA), random-walk, and autoregressive (AR) processes
 Joint stationarity, Ergodicity
o Joint SSS (JSSS) and WSS (JWSS) processes
o Ergodicity definitions
o Mean-ergodicity and Slutsky’s theorem
o Ergodicity in autocorrelation
o Examples of ergodic and non-ergodic processes
 Power Spectral Density, Wiener Filtering, Power and WSS Process Passing Through Linear Time-Invariant Systems
o Definitions of power spectral density (PSD), and continuous- and discrete-time white noises
o Joint stationarity of random processes
o Random process passing through linear time-variant and linear time-invariant (LTI) systems
o Linear MMSE estimation (Weiner filter)
o Parallel processing of disjoint frequency bands
 Processes with Independent Increments
o Random walk and first-order AR processes
o Levy processes (Poisson process, Wiener process)
 Markov Chains
o Transition matrix, stationary distribution
o Characterizing chains by state diagrams