1
Probability and Random Processes. Instructor: Prof. Mrityunjoy Chakraborty, Department of Electronics and Electrical Communication Engineering, IIT Kharagpur.
FREE
This course includes
Hours of videos
1111 years
Units & Quizzes
40
Unlimited Lifetime access
Access on mobile app
Certificate of Completion
This course covers lessons on Introduction to probability, Random variables, Sequence of random variables and convergence, and Random process. Topics covered include axioms of probability, the concepts of random variables, function of a random variable, mean and variance of a random variable, moments, characteristic function, two random variables, joint moments, joint characteristic functions, sequences of random variables, random process, spectral analysis, spectral estimation, and mean sequence estimation. (from nptel.ac.in)
Course Currilcum
- Lecture 01 – Introduction to the Theory of Probability Unlimited
- Lecture 02 – Axioms of Probability Unlimited
- Lecture 03 – Axioms of Probability (cont.) Unlimited
- Lecture 04 – Introduction to Random Variables Unlimited
- Lecture 05 – Probability Distributions and Density Functions Unlimited
- Lecture 06 – Conditional Distribution and Density Functions Unlimited
- Lecture 07 – Function of a Random Variable Unlimited
- Lecture 08 – Function of a Random Variable (cont.) Unlimited
- Lecture 09 – Mean and Variance of a Random Variable Unlimited
- Lecture 10 – Moments Unlimited
- Lecture 11 – Characteristic Function Unlimited
- Lecture 12 – Two Random Variables Unlimited
- Lecture 13 – Function of Two Random Variables Unlimited
- Lecture 14 – Function of Two Random Variables (cont.) Unlimited
- Lecture 15 – Correlation Covariance and Related Innver Unlimited
- Lecture 16 – Vector Space of Random Variables Unlimited
- Lecture 17 – Joint Moments Unlimited
- Lecture 18 – Joint Characteristic Functions Unlimited
- Lecture 19 – Joint Conditional Densities Unlimited
- Lecture 20 – Joint Conditional Densities (cont.) Unlimited
- Lecture 21 – Sequences of Random Variables Unlimited
- Lecture 22 – Sequences of Random Variables (cont.) Unlimited
- Lecture 23 – Correlation Matrices and their Properties Unlimited
- Lecture 24 – Correlation Matrices and their Properties (cont.) Unlimited
- Lecture 25 – Conditional Densities of Random Vectors Unlimited
- Lecture 26 – Characteristic Functions and Normality of a Random Vector Unlimited
- Lecture 27 – Chebyshev Inequality and Estimation of an Unknown Parameter Unlimited
- Lecture 28 – Central Limit Theorem Unlimited
- Lecture 29 – Introduction to Stochastic Process Unlimited
- Lecture 30 – Stationary Processes Unlimited
- Lecture 31 – Cyclostationary Processes Unlimited
- Lecture 32 – System with Random Process at Input Unlimited
- Lecture 33 – Ergodic Processes Unlimited
- Lecture 34 – Introduction to Spectral Analysis Unlimited
- Lecture 35 – Spectral Analysis (cont.) Unlimited
- Lecture 36 – Spectrum Estimation – Non-parametric Methods Unlimited
- Lecture 37 – Spectrum Estimation – Parametric Methods Unlimited
- Lecture 38 – Autoregressive Modeling and Linear Prediction Unlimited
- Lecture 39 – Linear Mean Square Estimation – Wiener (FIR) Filter Unlimited
- Lecture 40 – Adaptive Filtering – LMS Algorithm Unlimited
