Math 2.0

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Introduction to Probability Theory and Stochastic Processes (Tamil) KTQYT0AAAB

URIm2://res/KTQYT0AAAB
Kindcourse
CreatorsS Dharmaraja (instructor)
Date2024-07-19
MSC60-XX
Scopein
Idscourse 049tgcd06:111102518

Expressions

e1: edition

Languageta
Date2024-07-19
Contents
  • part 1: Week 1
  • session 1: Lecture 1 : Random Experiment, Sample Space and Sigma Field
  • session 2: Lecture 2 :Axiomatic Definition of Probability
  • session 3: Lecture 3 : Properties of Axiomatic Definition of Probability and Classical Definition of Probability
  • session 4: Lecture 4 : Conditional Probability, Independent Events and Baye's Rule
  • part 2: Week 2
  • session 5: Lecture 5 : Definition of Random Variable
  • session 6: Lecture 6 : Cumulative Distribution Function
  • session 7: Lecture 7 : Discrete and Continuous Type Random Variables
  • session 8: Lecture 8 : Mixed Type Random Variable
  • session 9: Lecture 9 : Function of a Random Variable
  • part 3: Week 3
  • session 10: Lecture 10 : Mean
  • session 11: Lecture 11 : Variance and higher order moments
  • session 12: Lecture 12 : Inequalities of Markov and Chebyshev
  • session 13: Lecture 13 : Generating functions
  • part 4: Week 4
  • session 14: Lecture 14 : Standard discrete distributions
  • session 15: Lecture 15 : Standard discrete distributions (continued)
  • session 16: Lecture 16 : Standard continuous distributions
  • session 17: Lecture 17 : Standard continuous distributions (continued)
  • part 5: Week 5
  • session 18: Lecture 18 : Definition and Joint Distribution of a Random Vector
  • session 19: Lecture 19 : Joint Probability Mass Function
  • session 20: Lecture 20 : Joint Probability Density Function
  • session 21: Lecture 21 : Independent Random Vector
  • part 6: Week 6
  • session 22: Lecture 22 : Distribution of functions of random variables (Discrete type)
  • session 23: Lecture 23 : Distribution of functions of random variables (Continuous type)
  • session 24: Lecture 24 : Conditional Distribution of Random Variables (Discrete type)
  • session 25: Lecture 25 : Conditional Distribution of Random Variables (Continuous type)
  • part 7: Week 7
  • session 26: Lecture 26 : Expectation for several random variables
  • session 27: Lecture 27 : Covariance and correlation coefficient
  • session 28: Lecture 28 : Generating functions for several random variables
  • session 29: Lecture 29 : Conditional Expectation
  • part 8: Week 8
  • session 30: Lecture 30 : Modes of convergence
  • session 31: Lecture 31 : Modes of convergence (contd)
  • session 32: Lecture 32 : Law of Large Numbers
  • session 33: Lecture 33 : Central Limit Theorem
  • part 9: Week 9
  • session 34: Lecture 34 : Definition and Classification of Stochastic Processes
  • session 35: Lecture 35 : Properties of Stochastic Processes
  • session 36: Lecture 36 : Properties of Stochastic Processes(Contd)
  • session 37: Lecture 37 : Standard Simple Stochastic Processes
  • part 10: Week 10
  • session 38: Lecture 38: Definition of Discrete Time Markov Chain (DTMC)
  • session 39: Lecture 39: Chapman-Kolmogorov Equation
  • session 40: Lecture 40: Classification of States
  • session 41: Lecture 41 : Classification of States (Contd)
  • session 42: Lecture 42 : Limiting Distribution
  • session 43: Lecture 43 : Stationary Distribution
  • session 44: Lecture 44 : Reducible Markov Chains
  • part 11: Week 11
  • session 45: Lecture 45 : Definition of Continuous Time Markov Chain (CTMC)
  • session 46: Lecture 46 : Infinitesimal Generator Matrix
  • session 47: Lecture 47 : Kolmogorov Differential Equations
  • session 48: Lecture 48 : Limiting Distribution and Steady-State Distribution
  • session 49: Lecture 49 : Birth Death Processes
  • part 12: Week 12
  • session 50: Lecture 50 : Introduction to Queueing Models and Kendall Notation
  • session 51: Lecture 51 : Single-Server Queueing Models
  • session 52: Lecture 52 : Single-Server Queueing Models (Contd)
  • session 53: Lecture 53 : Multi-Server Queueing Models

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