#1
February 4th, 2017, 01:36 PM
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IIT Delhi MOOC
Hi I am interested in knowing about Massive Open Online Course as well as the details of the Stochastic Processes course at IIT, Delhi through MOOC's?
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#2
February 4th, 2017, 02:17 PM
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Re: IIT Delhi MOOC
The Indian Institute of Technology Delhi is an open building establishment situated in Delhi, India. It is one of the IITs alongside other Indian Institutes of Technology establishments in India "MOOC" remains for Massive Open Online Course. Generally these online courses are educated by colleges all around the globe They are accessible for anybody with a web association. A portion of the well known MOOC suppliers incorporate Coursera, edX, Udacity, and FutureLearn. They band together with colleges, organizations, and teachers to give MOOCs. MOOCs are intended for an online group of onlookers, instructing basically through short (5–20 min.) pre-recorded video addresses. You watch these recordings on a week by week plan when it is advantageous for you. MOOCs likewise have understudy talk discussions, homework/assignments, and online tests or exams. Stochastic Processes at IIT, Delhi through MOOC's This course clarifications and compositions of stochastic procedures ideas which they requirement for their examinations and research. It likewise covers hypothetical ideas relating to taking care of different stochastic demonstrating. This course gives arrangement and properties of stochastic procedures, discrete and ceaseless time Markov chains, basic Markovian queueing models, uses of CTMC, martingales, Brownian movement, restoration forms, spreading forms, stationary and autoregressive procedures. Syllabus Week 1:Probability theory refresher Introduction to stochastic process Introduction to stochastic process (contd.) Week 2:Probability theory refresher (contd.) Problems in random variables and distributions Problems in Sequence of random variables Week 3efinition and simple stochastic process Definition, classification and Examples Simple stochastic processes Week 4iscrete-time Markov chains Introduction, Definition and Transition Probability Matrix Chapman-Kolmogorov Equations Classification of States and Limiting Distributions Week 5iscrete-time Markov chains (contd.) Limiting and Stationary Distributions Limiting Distributions, Ergodicity and stationary distributions Time Reversible Markov Chain, Application of Irreducible Markov chains in Queueing Models Reducible Markov Chains Week 6:Continuous-time Markov chains Definition, Kolmogrov Differential Equation and Infinitesimal Generator Matrix Limiting and Stationary Distributions, Birth Death Processes Poisson processes Week 7:Continuous-time Markov Chains (contd.) M/M/1 Queueing model Simple Markovian Queueing Models Week 8:Applications of CTMC Queueing networks Communication systems Stochastic Petri Nets Week 9:Martingales Conditional Expectation and filteration Definition and simple examples Week 10:Brownian Motion Definition and Properties Processes Derived from Brownian Motion Stochastic Differential Equation Week 11:Renewal Processes Renewal Function and Equation Generalized Renewal Processes and Renewal Limit Theorems Markov Renewal and Markov Regenerative Processes Non Markovian Queues Application of Markov Regenerative Processes Week 12:Branching Processes, Stationary and Autoregressive Processes |
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