Subject Code: CL5L317	Subject Name:  Computational Geoscience	L-T-P:  2-1-0	Credit: 3
Pre-requisite(s): Nil
Introduction to probability: random experiments, events, sample space, definitions of probability. Conditional probability and independence of events, Bayes theorm. Random variables, discrete and continuous probability distributions, joint probability distributions, conditional probability distributions. Mathematical expectation, moment generating and characteristic functions. Binomial,  Poisson, Normal, Gamma, Exponential, Hypergeometric, Multinomial, Chi-square, t, and F distributions. Introduction to statistical inference, sampling distributions, point and interval estimation, hypothesis testing involving one and two univariate populations. Linear models ANOVA. Linear and multiple regression. Introduction to multivariate techniques PCA, factor analysis, linear discriminant analysis, classification.
Text/Reference Books: W. H. Press, S. A. Teukolsky, W. T. Vetterling, & B. P. Flanner. Numerical Recipes in C/Fortran: The Art of Scientific Computing, Cambridge University Press Trauth, E. Sillmann, R. Gebbers, N. Marwan. MATLAB® Recipes for Earth Sciences, Springer   D Wilks.  Volume 100: Statistical Methods in the Atmospheric Sciences, 3rd Edition, Elsevier