Stochastic Processes. Instructor: Dr. S. Dharmaraja, Department of Mathematics, IIT Delhi.

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This course includes
Hours of videos

1083 years, 2 months

Units & Quizzes

39

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Certificate of Completion

This course explains and exposits concepts of stochastic processes which they need for their experiments and research. It also covers theoretical concepts pertaining to handling various stochastic modeling. This course provides classification and properties of stochastic processes, stationary processes, discrete and continuous time Markov chains and simple Markovian queueing models. (from nptel.ac.in)

Course Currilcum

    • Lecture 01 – Introduction to Stochastic Processes Unlimited
    • Lecture 02 – Introduction to Stochastic Processes (cont.) Unlimited
    • Lecture 03 – Problems in Random Variables and Distributions Unlimited
    • Lecture 04 – Problems in Sequences of Random Variables Unlimited
    • Lecture 05 – Definition, Classification and Examples Unlimited
    • Lecture 06 – Simple Stochastic Processes Unlimited
    • Lecture 07 – Stationary Processes Unlimited
    • Lecture 08 – Autoregressive Processes Unlimited
    • Lecture 09 – Introduction, Definition and Transition Probability Matrix Unlimited
    • Lecture 10 – Chapman-Kolmogorov Equations Unlimited
    • Lecture 11 – Classification of States and Limiting Distributions Unlimited
    • Lecture 12 – Limiting and Stationary Distributions Unlimited
    • Lecture 13 – Limiting Distributions, Ergodicity and Stationary Distributions Unlimited
    • Lecture 14 – Time Reversible Markov Chain, Application of Irreducible Markov Chain in Queueing Models Unlimited
    • Lecture 15 – Reducible Markov Chains Unlimited
    • Lecture 16 – Definition, Kolmogorov Differential Equations and Infinitesimal Generator Matrix Unlimited
    • Lecture 17 – Limiting and Stationary Distributions, Birth Death Processes Unlimited
    • Lecture 18 – Poisson Processes Unlimited
    • Lecture 19 – M/M/1 Queueing Model Unlimited
    • Lecture 20 – Simple Markovian Queuing Models Unlimited
    • Lecture 21 – Queuing Networks Unlimited
    • Lecture 22 – Communication Systems Unlimited
    • Lecture 23 – Stochastic Petri Nets Unlimited
    • Lecture 24 – Conditional Expectation and Filtration Unlimited
    • Lecture 25 – Definition and Simple Examples Unlimited
    • Lecture 26 – Definition and Properties Unlimited
    • Lecture 27 – Processes Derived from Brownian Motion Unlimited
    • Lecture 28 – Stochastic Differential Equations Unlimited
    • Lecture 29 – Ito Integrals Unlimited
    • Lecture 30 – Ito Formula and its Variants Unlimited
    • Lecture 31 – Some Important Stochastic Differential Equations and their Solutions Unlimited
    • Lecture 32 – Renewal Function and Renewal Equation Unlimited
    • Lecture 33 – Generalized Renewal Processes and Renewal Limit Theorems Unlimited
    • Lecture 34 – Markov Renewal and Markov Regenerative Processes Unlimited
    • Lecture 35 – Non-Markovian Queues Unlimited
    • Lecture 36 – Non-Markovian Queues (cont.) Unlimited
    • Lecture 37 – Application of Markov Regenerative Processes Unlimited
    • Lecture 38 – Galton-Watson Process Unlimited
    • Lecture 39 – Markovian Branching Process Unlimited