Monday 4:30 pm - 5:20 pm
Tuesday 11:00 am - 12:50 pm
Wednesday 4:30 pm - 5:20 pm
Random experiments. Sample space. Concept of classical theory of probability. Elements of combinatorics. Related problems from classical theory.
Probability of union of events. Conditional probability and independence of two or more events. Total Probability. Bayes’ Theorem.
Random variables. Discrete and continuous random variables. Expectation, Variance. Special distributions including binomial, Poisson and normal distribution. Joint distributions of two random variables. Conditional distributions. Independence of two random variables. Covariance and correlation coefficient. Conditional expectation and variance.
Law of large numbers and central limit theorem (statements only).
Devore, JL: Probability and Statistics for Engineering and the Sciences
Rukmangadachari, E & Reddy, EK: Probability and Statistics
Ross, SM: Introduction to Probability Models
Assignments - 25%
Mid-term exam - 25%
End-term exam - 50%