MA3355 RANDOM PROCESSES AND LINEAR ALGEBRA
AU SYLLABUS
MA3355 RANDOM PROCESSES AND LINEAR ALGEBRA
ANNA UNIVERSITY SYLLABUS
MA3355 RANDOM PROCESSES AND LINEAR ALGEBRA L T P C
COURSE OBJECTIVES :
 To introduce the basic notions of vector spaces which will then be used to solve related
 To understand the concepts of vector space, linear transformations , inner product spaces and .
 To provide necessary basic concepts in probability and random processes for applications such as random signals, linear systems in communication
 To provide necessary basics in probability that are relevant in applications such as random signals, linear systems in communication engineering.
 To understand the basic concepts of probability, one and two dimensional random
 variables and to introduce some standard distributions applicable to engineering which can describe real life phenomenon.
UNIT – I PROBABILITY AND RANDOM VARIABLES 9 + 3
Axioms of probability – Conditional probability – Baye’s theorem – Discrete and continuous random variables – Moments – Moment generating functions – Binomial, Poisson, Geometric, Uniform, Exponential and Normal distributions – Functions of a random variable.
UNIT – II TWO – DIMENSIONAL RANDOM VARIABLES 9 + 3
Joint distributions – Marginal and conditional distributions – Covariance – Correlation and linear regression – Transformation of random variables – Central limit theorem (for independent and identically distributed random variables).
UNIT – III RANDOM PROCESSES 9 + 3
Classification – Stationary process – Markov process – Poisson process – Discrete parameter Markov chain – Chapman Kolmogorov equations (Statement only) – Limiting distributions .
UNIT – IV VECTOR SPACES 9 + 3
Vector spaces – Subspaces – Linear combinations and linear system of equations – Linear independence and linear dependence – Bases and dimensions.
UNIT – V LINEAR TRANSFORMATION AND INNER PRODUCT SPACES 9 + 3
Linear transformation – Null spaces and ranges – Dimension theorem – Matrix representation of a linear transformations – Inner product – Norms – Gram Schmidt orthogonalization process – Adjoint of linear operations – Least square approximation.
COURSE OUTCOMES :
Upon successful completion of the course, students will be able to:
CO1:Explain the fundamental concepts of advanced algebra and their role in modern mathematics and applied contexts.
CO2:Demonstrate accurate and efficient use of advanced algebraic techniques. CO3:Apply the concept of random processes in engineering disciplines.
CO3:Understand the fundamental concepts of probability with a thorough knowledge of standard distributions that can describe certain reallife phenomenon.
CO4: Understand the basic concepts of one and two dimensional random variables and apply them to model engineering problems.
TEXTBOOKS :

 Gross, , Shortle, J.F, Thompson, J.M and Harris. C.M., “Fundamentals of Queueing Theory”, Wiley Student 4th Edition, 2014.
 Ibe, C., “Fundamentals of Applied Probability and Random Processes”, Elsevier,1st Indian Reprint, 2007.
 A.H., Insel. A.J. and Spence. L., “Linear Algebra”, Prentice Hall of India, New Delhi, 4^{th} Edition, 2004.
REFERENCE BOOKS :

 Hsu, “Schaum’s Outline of Theory and Problems of Probability, Random Variables and Random Processes”, Tata McGraw Hill Edition, New Delhi,
 Trivedi, S., “Probability and Statistics with Reliability, Queueing and Computer Science Applications”, 2nd Edition, John Wiley and Sons, 2002.
 Yates, D. and Goodman. D. J., “Probability and Stochastic Processes”, 2nd Edition, Wiley India Pvt. Ltd., Bangalore, 2012.
 B. Hill. D.R., “Introductory Linear Algebra”, Pearson Education, New Delhi, First Reprint, 2009.
 S., “Linear Algebra – A Geometric Approach”, Prentice – Hall of India, New Delhi, Reprint, 2010.
 G., “Linear Algebra and its applications”, Thomson (Brooks/Cole), New Delhi, 2005.
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