Master BCS301 VTU Notes : Mathematics for Computer Science with our expert VTU notes. Explore probability distributions, Markov chains, and statistical inference designed for the 3rd sem CSE 2022 scheme Also Check Other Notes for CSE Branch
Master BCS301 VTU Notes : Mathematics for Computer Science with our expert VTU notes. Explore probability distributions, Markov chains, and statistical inference designed for the 3rd sem CSE 2022 scheme Also Check Other Notes for CSE Branch
Review of basic probability theory. Random variables (discrete and continuous), probability mass and density functions. Mathematical expectation, mean and variance. Binomial, Poisson and normal distributions- problems (derivations for mean and standard deviation for Binomial and Poisson distributions only)-Illustrative examples. Exponential distribution.
Joint probability distribution: Joint Probability distribution for two discrete randomvariables, expectation, covariance and correlation.
Markov Chain: Introduction to Stochastic Process, Probability Vectors, Stochastic matrices, Regular stochastic matrices, Markov chains, Higher transition probabilities, Stationary distribution of Regular Markov chains and absorbing states.
Introduction, sampling distribution, standard error, testing of hypothesis, levels of significance, test of significances, confidence limits, simple sampling of attributes, test of significance for large samples, comparison of large samples.
Sampling variability, central limit theorem and confidence limits for unknown mean. Test of Significance for means of two small samples, students ‘t’ distribution, Chi-square distribution as a test of goodness of fit. F-Distribution.
Principles of experimentation in design, Analysis of completely randomized design, randomized block design. The ANOVA Technique, Basic Principles of ANOVA, One-way ANOVA, Two-way ANOVA, Latin-square Design, and Analysis of Co-Variance.
BIS654C
BCS654A
BCS613D
BCS613C
BCS3012Mod
BCEDK103
BCSL305
BCS30122550question
BCS303
XYZS301
