Course info

MTH202: Probability and statistics • Recapitulation: Counting (urn, coins, cards). • Axiomatic approach to probability, conditional probability, independence of events. • Discrete random variables, probability mass function, some standard discrete distributions and examples. • Continuous random variables, probability density function, some standard continuous distributions and examples. • Bivariate distributions (discrete and continuous), marginal and conditional distributions, covariance, correlation coefficient. • Moments, Markov’s inequality, Chebychev’s inequality. • Sums of independent random variables, law of large numbers, central limit theorem • A glimpse into estimation theory (maximum likelihood estimation, method of moments) and testing of hypothesis. Recommended Reading • K. L. Chung and F. AitSahila, Elementary Probability Theory, Springer (2004). • W. Feller, Introduction to Probability and Statistics, Wiley (1956). • S. Ross, A First Course in Probability, Pearson Education Inc. (2006). • R. Isaac, The Pleasures of Probability, Springer (Undergraduate Texts in Mathematics) (1995).