Stochastic Processes video lectures, S. Dharmaraja of IIT Delhi

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Stochastic Processes

Lecture 1: Mod-01 Lec-01 Introduction to Stochastic Processes

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Lecture Details :

Stochastic Processes by Dr. S. Dharmaraja, Department of Mathematics, IIT Delhi. For more details on NPTEL visit http://nptel.iitm.ac.in

Course Description :

Contents:
Probability Theory Refresher: Axiomatic construction of probability spaces, random variables and vectors, probability distributions, functions of random variables; mathematical expectations, transforms and generating functions, modes of convergence of sequences of random variables, laws of large numbers, central limit theorem.
Introduction to Stochastic Processes (SPs): Definition and examples of SPs, classification of random processes according to state space and parameter space, types of SPs, elementary problems.
Discrete-time Markov Chains (MCs): Definition and examples of MCs, transition probability matrix, Chapman-Kolmogorov equations; calculation of n-step transition probabilities, limiting probabilities, classification of states, ergodicity, stationary distribution, transient MC; random walk and gamblerís ruin problem, applications.

Continuous-time Markov Chains (MCs): Kolmogorov- Feller differential equations, infinitesimal generator, Poisson process, birth-death process, Applications to queueing theory, inventory analysis, communication networks, finance and biology.
Brownian Motion: Wiener process as a limit of random walk; first -passage time and other problems, applications to finance.
Branching Processes: Definition and examples branching processes, probability generating function, mean and variance, Galton-Watson branching process, probability of extinction.

Renewal Processes: Renewal function and its properties, elementary and key renewal theorems, cost/rewards associated with renewals, Markov renewal and regenerative processes, applications.
Stationary Processes: Weakly stationary and strongly stationary processes, moving average and auto regressive processes.
Martingales: Conditional expectations, definition and examples of martingales, inequality, convergence and smoothing properties, applications in finance.

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