Math 2.0

Resources / course

Probability Theory and Applications H7H9N40DYH

URIm2://res/H7H9N40DYH
Kindcourse
CreatorsPrabha Sharma (instructor)
Date2014-11-07
MSC00-XX
Scopein
Idscourse 05pjsgx75:111104079

Expressions

e1: edition

Languageen
Date2014-11-07
Contents
  • part 1: Probability Theory and Applications
  • session 1: Lecture-01-Basic principles of counting
  • session 2: Lecture-02-Sample space , events, axioms of probability
  • session 3: Lecture-03-Conditional probability, Independence of events.
  • session 4: Lecture-04-Random variables, cumulative density function, expected value
  • session 5: Lecture-05-Discrete random variables and their distributions
  • session 6: Lecture-06-Discrete random variables and their distributions
  • session 7: Lecture-07-Discrete random variables and their distributions
  • session 8: Lecture-08-Continuous random variables and their distributions.
  • session 9: Lecture-09-Continuous random variables and their distributions.
  • session 10: Lecture-10-Continuous random variables and their distributions.
  • session 11: Lecture-11-Function of random variables, Momement generating function
  • session 12: Lecture-12-Jointly distributed random variables, Independent r. v. and their sums
  • session 13: Lecture-13-Independent r. v. and their sums.
  • session 14: Lecture-14-Chi – square r. v., sums of independent normal r. v., Conditional distr.
  • session 15: Lecture-15 Conditional disti, Joint distr. of functions of r. v., Order statistics
  • session 16: Lecture-16-Order statistics, Covariance and correlation.
  • session 17: Lecture-17-Covariance, Correlation, Cauchy- Schwarz inequalities, Conditional expectation.
  • session 18: Lecture-18-Conditional expectation, Best linear predictor
  • session 19: Lecture-19-Inequalities and bounds.
  • session 20: Lecture-20-Convergence and limit theorems
  • session 21: Lecture-21-Central limit theorem
  • session 22: Lecture-22-Applications of central limit theorem
  • session 23: Lecture-23-Strong law of large numbers, Joint mgf.
  • session 24: Lecture-24-Convolutions
  • session 25: Lecture-25-Stochastic processes: Markov process.
  • session 26: Lecture-26-Transition and state probabilities.
  • session 27: Lecture-27-State prob., First passage and First return prob
  • session 28: Lecture-28-First passage and First return prob. Classification of states.
  • session 29: Lecture-29-Random walk, periodic and null states.
  • session 30: Lecture-30-Reducible Markov chains
  • session 31: Lecture-31-Time reversible Markov chains
  • session 32: Lecture-32-Poisson Processes
  • session 33: Lecture-33-Inter-arrival times, Properties of Poisson processes
  • session 34: Lecture-34-Queuing Models: M/M/I, Birth and death process, Little’s formulae
  • session 35: Lecture-35-Analysis of L, Lq ,W and Wq , M/M/S model
  • session 36: Lecture-36-M/M/S , M/M/I/K models
  • session 37: Lecture-37-M/M/I/K and M/M/S/K models
  • session 38: Lecture-38-Application to reliability theory failure law
  • session 39: Lecture-39-Exponential failure law, Weibull law
  • session 40: Lecture-40-Reliability of systems

Catalogue records