# InfoCoBuild

## Advanced Topics in Probability and Random Processes

Advanced Topics in Probability and Random Processes. Instructor: Prof. P. K. Bora, Department of Electronics and Electrical Engineering, IIT Guwahati. The course will cover mainly two broad areas: (1) the concepts of the convergence a sequence of random variables leading to the explanation of important concepts like the laws of large numbers, central limit theorem; and (2) Markov chains that include the analysis of discrete and continuous time Markov Chains and their applications. (from nptel.ac.in)

 Lecture 01 - Probability Basics

Go to the Course Home or watch other lectures:

 Introduction to Probability and Random Variable Lecture 01 - Probability Basics Lecture 02 - Random Variable Lecture 03 - Random Variable (cont.) Random Process Basics and Infinite Sequence of Events Lecture 04 - Random Vectors and Random Processes Lecture 05 - Infinite Sequence of Events Lecture 06 - Infinite Sequence of Events (cont.) Convergence of a Sequence of Random Variables Lecture 07 - Convergence of a Sequence of Random Variables Lecture 08 - Weak Convergence Lecture 09 - Weak Convergence (cont.) Applications of Convergence Theory Lecture 10 - Laws of Large Numbers Lecture 11 - Central Limit Theorem Lecture 12 - Large Deviation Theory Markov Chain Lecture 13 - Cramer's Theorem for Large Deviation Lecture 14 - Introduction to Markov Processes Discrete Time Markov Chain Lecture 15 - Discrete Time Markov Chain 1 Lecture 16 - Discrete Time Markov Chain 2 Lecture 17 - Discrete Time Markov Chain 3 Lecture 18 - Discrete Time Markov Chain 4 Lecture 19 - Discrete Time Markov Chain 5 Continuous Time Markov Chain Lecture 20 - Continuous Time Markov Chain 1 Lecture 21 - Continuous Time Markov Chain 2 Lecture 22 - Continuous Time Markov Chain 3 Martingale Process Lecture 23 - Martingale Process Lecture 24 - Martingale Process (cont.)