MA2103 Probability, Stochastic Process and Statistics - [Only for 2024 batch onwards]
Course Details
Probability:
Probability models and axioms, conditioning and Bayes' rule, independence, discrete random variables; probability mass functions; expectations, multiple discrete random variables, joint PMFs, expectations, conditioning, independence, continuous random variables, probability density functions, expectations, multiple continuous random variables, continuous Bayes rule, derived distributions, convolution, covariance and correlation, iterated expectations.
Stochastic processes:
Bernoulli process, Poisson process, Markov chains.
Statistics:
Statistical inference, point estimators, parameter estimators, test of hypotheses
Course References:
Text:
D. Bertsekas and J. Tsitsiklis, Introduction to Probability, 2nd ed, Athena Scientific,2008.
Reference:
1. K.L. Chung, Elementary Probability Theory with Stochastic Process, Springer Verlag, 1974.
2. A. Drake, Fundamentals of Applied Probability Theory. McGraw-Hill, 1967.
3. O. Ibe, Fundamentals of Applied Probability and Random Processes, Academic Press, 2005.
4. S. Ross, A First Course in Probability. 8th ed. Prentice Hall, 2009.