MA3355 RANDOM PROCESSES AND LINEAR ALGEBRA
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 real-life phenomenon.
CO4: Understand the basic concepts of one and two dimensional random variables and apply them to model engineering problems.
- 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, 4th 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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