NOC:Introduction to Probability Theory and Stochastic Processes (Tamil)

Lecture 1 - Random Experiment, Sample Space and Sigma Field

Lecture 2 - Axiomatic Definition of Probability

Lecture 3 - Properties of Axiomatic Definition of Probability and Classical Definition of Probability

Lecture 4 - Conditional Probability, Independent Events and Baye's Rule

Lecture 5 - Definition of Random Variable

Lecture 6 - Cumulative Distribution Function

Lecture 7 - Discrete and Continuous Type Random Variables

Lecture 8 - Mixed Type Random Variable

Lecture 9 - Function of a Random Variable

Lecture 10 - Mean

Lecture 11 - Variance and Higher Order Moments

Lecture 12 - Inequalities of Markov and Chebyshev

Lecture 13 - Generating Functions

Lecture 14 - Standard Discrete Distributions

Lecture 15 - Standard Discrete Distributions (Continued...)

Lecture 16 - Standard Continuous Distributions

Lecture 17 - Standard Continuous Distributions (Continued...)

Lecture 18 - Definition and Joint Distribution of a Random Vector

Lecture 19 - Joint Probability Mass Function

Lecture 20 - Joint Probability Density Function

Lecture 21 - Independent Random Vector

Lecture 22 - Distribution of Functions of Random Variables (Discrete Type)

Lecture 23 - Distribution of Functions of Random Variables (Continuous Type)

Lecture 24 - Conditional Distribution of Random Variables (Discrete Type)

Lecture 25 - Conditional Distribution of Random Variables (Continuous Type)

Lecture 26 - Expectation for Several Random Variables

Lecture 27 - Covariance and Correlation Coefficient

Lecture 28 - Generating Functions for Several Random Variables

Lecture 29 - Conditional Expectation

Lecture 30 - Modes of Convergence

Lecture 31 - Modes of Convergence (Continued...)

Lecture 32 - Law of Large Numbers

Lecture 33 - Central Limit Theorem

Lecture 34 - Definition and Classification of Stochastic Processes

Lecture 35 - Properties of Stochastic Processes

Lecture 36 - Properties of Stochastic Processes (Continued...)

Lecture 37 - Standard Simple Stochastic Processes

Lecture 38 - Definition of Discrete Time Markov Chain (DTMC)

Lecture 39 - Chapman-Kolmogorov Equation

Lecture 40 - Classification of States

Lecture 41 - Classification of States (Continued...)

Lecture 42 - Limiting Distribution

Lecture 43 - Stationary Distribution

Lecture 44 - Reducible Markov Chains

Lecture 45 - Definition of Continuous Time Markov Chain (CTMC)

Lecture 46 - Infinitesimal Generator Matrix

Lecture 47 - Kolmogorov Differential Equations

Lecture 48 - Limiting Distribution and Steady-State Distribution

Lecture 49 - Birth Death Processes

Lecture 50 - Introduction to Queueing Models and Kendall Notation

Lecture 51 - Single-Server Queueing Models

Lecture 52 - Single-Server Queueing Models (Continued...)

Lecture 53 - Multi-Server Queueing Models