NOC:Stochastic Control and Communication

Lecture 1 - Decision Making under Uncertainty

Lecture 2 - Expected Utility Theory - I

Lecture 3 - Expected Utility Theory - II

Lecture 4 - Expected Utility Theory - III

Lecture 5 - Role of Information in Decision Making

Lecture 6 - State Space Modelling of Sequential Decision Making, Example of Inventory Control

Lecture 7 - Inventory Control Problem (Continued...)

Lecture 8 - Policy-A Closed Loop Solution to Stochastic Control Problem

Lecture 9 - Introduction to Markov Decision Processes (MDP)

Lecture 10 - Types of Policy in MDP

Lecture 11 - Interpreting randomised decision rules

Lecture 12 - Stationary Transition Probability: State Diagram Representation and example of Markov policies

Lecture 13 - Example of History Dependent Policies

Lecture 14 - Complexity of the problem using brute force approach

Lecture 15 - Principle of Optimality

Lecture 16 - Dynamic Programming Algorithm

Lecture 17 - DP Algo applied to Inventory Control Problem

Lecture 18 - DP Algo applied to Inventory Control Problem (Continued...)

Lecture 19 - DP Algo applied to Inventory Control Problem (Continued...)

Lecture 20 - Optimal Stopping Problem

Lecture 21 - Optimal Stopping Example: Secretary Problem

Lecture 22 - Optimal Stopping Example: Secretary Problem (Continued...)

Lecture 23 - Optimal Stopping Example: Secretary Problem (Continued...)

Lecture 24 - Linear System Quadratic Cost Problem

Lecture 25 - Linear System Quadratic Cost Problem (Continued...)

Lecture 26 - Solving it via DP algorithm (Continued...)

Lecture 27 - Equivalence between Optimal HR Policyand optimal Markov Deterministic Policy

Lecture 28 - Stochastic Control under incomplete state information

Lecture 29 - Stochastic Control under incomplete state information (Continued...)

Lecture 30 - Stochastic Control under incomplete state information: Example

Lecture 31 - Stochastic Control under incomplete state information: Example (Continued...)

Lecture 32 - Stochastic Control under incomplete state information: Example (Continued...)

Lecture 33 - Stochastic Control under incomplete state information: Example (Continued...)

Lecture 34 - LQ systems with Imperfect Information - I

Lecture 35 - LQ systems with Imperfect Information - II

Lecture 36 - LQ systems with Imperfect Information - III

Lecture 37 - LQ systems with Imperfect Information - IV

Lecture 38 - Filtering - I

Lecture 39 - Filtering - II

Lecture 40 - Kalman Filtering - I

Lecture 41 - Kalman Filtering - II

Lecture 42 - Kalman Filtering - III

Lecture 43 - Belief State Formulation - I

Lecture 44 - Belief State Formulation - II

Lecture 45 - Information Structures - I

Lecture 46 - Information Structures - II

Lecture 47 - Witsenhausen Problem - I

Lecture 48 - Witsenhausen Problem - II

Lecture 49 - Witsenhausen Problem - III

Lecture 50 - Witsenhausen Problem - IV

Lecture 51 - Witsenhausen Problem - V

Lecture 52 - Witsenhausen Problem - VI

Lecture 53 - Witsenhausen Problem - VII

Lecture 54 - Team Decision Theory - I

Lecture 55 - Team Decision Theory - II

Lecture 56 - Team Decision Theory - III

Lecture 57 - Team Decision Theory - IV

Lecture 58 - Team Decision Theory - V

Lecture 59 - Team Decision Theory - VI

Lecture 60 - Team Decision Theory - VII

Lecture 61 - Communication Theory - I

Lecture 62 - Communication Theory - II

Lecture 63 - Communication Theory - III

Lecture 64 - Communication Theory - IV

Lecture 65 - Communication Theory - V