Lecture 24 of 39Martingales
Conditional Expectation and Filtration
Dr. S. Dharmaraja · IIT Delhi
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- Stochastic Processes, IIT Delhi (at NPTEL)
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All the lectures
- Introduction to Stochastic Processes
- Introduction to Stochastic Processes (Contd.)
- Problems in Random Variables and Distributions
- Problems in Sequences of Random Variables
- Definition, Classification and Examples
- Simple Stochastic Processes
- Stationary Processes
- Autoregressive Processes
- Introduction, Definition and Transition Probability Matrix
- Chapman-Kolmogrov Equations
- Classification of States and Limiting Distributions
- Limiting and Stationary Distributions
- Limiting Distributions, Ergodicity and Stationary Distributions
- Time Reversible Markov Chain, Application of Irreducible Markov Chain in Queueing Models
- Reducible Markov Chains
- Definition, Kolmogrov Differential Equations and Infinitesimal Generator Matrix
- Limiting and Stationary Distributions, Birth Death Processes
- Poisson Processes
- M/M/1 Queueing Model
- Simple Markovian Queueing Models
- Queueing Networks
- Communication Systems
- Stochastic Petri Nets
- Conditional Expectation and Filtration
- Definition and Simple Examples
- Definition and Properties
- Processes Derived from Brownian Motion
- Stochastic Differential Equations
- Ito Integrals
- Ito Formula and its Variants
- Some Important SDE`s and Their Solutions
- Renewal Function and Renewal Equation
- Generalized Renewal Processes and Renewal Limit Theorems
- Markov Renewal and Markov Regenerative Processes
- Non Markovian Queues
- Non Markovian Queues Cont,,
- Application of Markov Regenerative Processes
- Galton-Watson Process
- Markovian Branching Process