Media Storage Type : 32 GB USB Stick
NPTEL Subject Matter Expert : Dr. S. Dharmaraja
NPTEL Co-ordinating Institute : IIT Delhi
NPTEL Lecture Count : 124
NPTEL Course Size : 13 GB
NPTEL PDF Text Transcription : Not Available
NPTEL Subtitle Transcription : Partially Available
Lecture Titles:
Lecture 1 - Introduction and motivation for studying stochastic processes
Lecture 2 - Probability space and conditional probability
Lecture 3 - Random variable and cumulative distributive function
Lecture 4 - Discrete Uniform Distribution, Binomial Distribution, Geometric Distribution, Continuous Uniform Distribution, Exponential Distribution, Normal Distribution and Poisson Distribution
Lecture 5 - Joint Distribution of Random Variables
Lecture 6 - Independent Random Variables, Covariance and Correlation Coefficient and Conditional Distribution
Lecture 7 - Conditional Expectation and Covariance Matrix
Lecture 8 - Generating Functions, Law of Large Numbers and Central Limit Theorem
Lecture 9 - Problems in Random variables and Distributions
Lecture 10 - Problems in Random variables and Distributions (Continued...)
Lecture 11 - Problems in Random variables and Distributions (Continued...)
Lecture 12 - Problems in Random variables and Distributions (Continued...)
Lecture 13 - Problems in Sequences of Random Variables
Lecture 14 - Problems in Sequences of Random Variables (Continued...)
Lecture 15 - Problems in Sequences of Random Variables (Continued...)
Lecture 16 - Problems in Sequences of Random Variables (Continued...)
Lecture 17 - Definition of Stochastic Processes, Parameter and State Spaces
Lecture 18 - Classification of Stochastic Processes
Lecture 19 - Examples of Discrete Time Markov Chain
Lecture 20 - Examples of Discrete Time Markov Chain (Continued...)
Lecture 21 - Bernoulli Process
Lecture 22 - Poisson Process
Lecture 23 - Poisson Process (Continued...)
Lecture 24 - Simple Random Walk and Population Processes
Lecture 25 - Introduction to Discrete time Markov Chain
Lecture 26 - Introduction to Discrete time Markov Chain (Continued...)
Lecture 27 - Examples of Discrete time Markov Chain
Lecture 28 - Examples of Discrete time Markov Chain (Continued...)
Lecture 29 - Introduction to Chapman-Kolmogorov equations
Lecture 30 - State Transition Diagram and Examples
Lecture 31 - Examples
Lecture 32 - Introduction to Classification of States and Periodicity
Lecture 33 - Closed set of States and Irreducible Markov Chain
Lecture 34 - First Passage time and Mean Recurrence Time
Lecture 35 - Recurrent State and Transient State
Lecture 36 - Introduction and example of Classification of states
Lecture 37 - Example of Classification of states (Continued...)
Lecture 38 - Example of Classification of states (Continued...)
Lecture 39 - Example of Classification of states (Continued...)
Lecture 40 - Introduction and Limiting Distribution
Lecture 41 - Example of Limiting Distribution and Ergodicity
Lecture 42 - Stationary Distribution and Examples
Lecture 43 - Examples of Stationary Distributions
Lecture 44 - Time Reversible Markov Chain and Examples
Lecture 45 - Definition of Reducible Markov Chains and Types of Reducible Markov Chains
Lecture 46 - Stationary Distributions and Types of Reducible Markov chains
Lecture 47 - Type of Reducible Markov Chains (Continued...)
Lecture 48 - Gambler's Ruin Problem
Lecture 49 - Introduction to Continuous time Markov Chain
Lecture 50 - Waiting time Distribution
Lecture 51 - Chapman-Kolmogorov Equation
Lecture 52 - Infinitesimal Generator Matrix
Lecture 53 - Introduction and Example Of Continuous time Markov Chain
Lecture 54 - Limiting and Stationary Distributions
Lecture 55 - Time reversible CTMC and Birth Death Process
Lecture 56 - Steady State Distributions, Pure Birth Process and Pure Death Process
Lecture 57 - Introduction to Poisson Process
Lecture 58 - Definition of Poisson Process
Lecture 59 - Superposition and Deposition of Poisson Process
Lecture 60 - Compound Poisson Process and Examples
Lecture 61 - Introduction to Queueing Systems and Kendall Notations
Lecture 62 - M/M/1 Queueing Model
Lecture 63 - Little's Law, Distribution of Waiting Time and Response Time
Lecture 64 - Burke's Theorem and Simulation of M/M/1 queueing Model
Lecture 65 - M/M/c Queueing Model
Lecture 66 - M/M/1/N Queueing Model
Lecture 67 - M/M/c/K Model, M/M/c/c Loss System, M/M/? Self Service System
Lecture 68 - Transient Solution of Finite Birth Death Process and Finite Source Markovian Queueing Model
Lecture 69 - Queueing Networks Characteristics and Types of Queueing Networks
Lecture 70 - Tandem Queueing Networks
Lecture 71 - Stationary Distribution and Open Queueing Network
Lecture 72 - Jackson's Theorem, Closed Queueing Networks, Gordon and Newell Results
Lecture 73 - Wireless Handoff Performance Model and System Description
Lecture 74 - Description of 3G Cellular Networks and Queueing Model
Lecture 75 - Simulation of Queueing Systems
Lecture 76 - Definition and Basic Components of Petri Net and Reachability Analysis
Lecture 77 - Arc Extensions in Petri Net, Stochastic Petri Nets and examples
Lecture 78 - Generalized Stochastic Petri Net
Lecture 79 - Generalized Stochastic Petri Net (Continued...)
Lecture 80 - Conditional Expectation and Examples
Lecture 81 - Filtration in Discrete time
Lecture 82 - Remarks of Conditional Expectation and Adaptabilty
Lecture 83 - Definition and Examples of Martingale
Lecture 84 - Examples of Martingale (Continued...)
Lecture 85 - Examples of Martingale (Continued...)
Lecture 86 - Doob's Martingale Process, Sub martingale and Super Martingale
Lecture 87 - Definition of Brownian Motion
Lecture 88 - Definition of Brownian Motion (Continued...)
Lecture 89 - Properties of Brownian Motion
Lecture 90 - Processes Derived from Brownian Motion
Lecture 91 - Processes Derived from Brownian Motion (Continued...)
Lecture 92 - Processes Derived from Brownian Motion (Continued...)
Lecture 93 - Stochastic Differential Equations
Lecture 94 - Stochastic Differential Equations (Continued...)
Lecture 95 - Stochastic Differential Equations (Continued...)
Lecture 96 - Ito Integrals
Lecture 97 - Ito Integrals (Continued...)
Lecture 98 - Ito Integrals (Continued...)
Lecture 99 - Renewal Function and Renewal Equation
Lecture 100 - Renewal Function and Renewal Equation (Continued...)
Lecture 101 - Renewal Function and Renewal Equation (Continued...)
Lecture 102 - Generalized Renewal Processes and Renewal Limit Theorems
Lecture 103 - Generalized Renewal Processes and Renewal Limit Theorems (Continued...)
Lecture 104 - Generalized Renewal Processes and Renewal Limit Theorems (Continued...)
Lecture 105 - Markov Renewal and Markov Regenerative Processes
Lecture 106 - Markov Renewal and Markov Regenerative Processes (Continued...)
Lecture 107 - Markov Renewal and Markov Regenerative Processes (Continued...)
Lecture 108 - Markov Renewal and Markov Regenerative Processes (Continued...)
Lecture 109 - Non Markovian Queues
Lecture 110 - Non Markovian Queues (Continued...)
Lecture 111 - Non Markovian Queues (Continued...)
Lecture 112 - Stationary Processes
Lecture 113 - Stationary Processes (Continued...)
Lecture 114 - Stationary Processes (Continued...)
Lecture 115 - Stationary Processes (Continued...) and Ergodicity
Lecture 116 - G1/M/1 queue
Lecture 117 - G1/M/1 queue (Continued...)
Lecture 118 - G1/M/1/N queue and examples
Lecture 119 - Galton-Watson Process
Lecture 120 - Examples and Theorems
Lecture 121 - Theorems and Examples (Continued...)
Lecture 122 - Markov Branching Process
Lecture 123 - Markov Branching Process Theorems and Properties
Lecture 124 - Markov Branching Process Theorems and Properties (Continued...)