Here are the slides from Lectures

Please read Section 1.4 from the notes:

Please attempt Ex35, Ex36, Ex37.

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# Category: Uncategorized

## Infinite Time Horizon MDP

## Markov Decision Processes

## Markov Chains

## Dynamic Programming

## Stochastic Control 2020

## Kalman Filter

## Mixing Times

## Diffusion Control Problems

## Notes for Stochastic Control 2019

## Markov’s Inequality, Chebychev’s Inequality and the Weak Law of Large Numbers

Here are the slides from Lectures

Please read Section 1.4 from the notes:

Please attempt Ex35, Ex36, Ex37.

Here are the slides from Lectures

3_Markov Decision Processes [pdf]

Please read Section 1.3 from the notes:

Please attempt exercises Ex22, Ex23, Ex24, Ex25.

Here are the slides from Lectures

Please read Section 1.2 from the notes:

Slides from Lectures are here:

Please read Section 1.1 of the notes:

Stochastic Control Notes [pdf]

Please attempt exercises **Ex3, Ex5 **and** Ex6** from the notes.

Another year of MATH69122! — aka Stochastic Control.

This year, I will try to keep updating PDFs with slides and notes for each lecture. I’ll keep notes for the course in the “PDF” tab above. These are also here:

Here is a rough plan for each week of lectures:

Kalman filtering (and filtering in general) considers the following setting: we have a sequence of states , which evolves under random perturbations over time. Unfortunately we cannot observe , we can only observe some noisy function of , namely, . Our task is to find the best estimate of given our observations of . Continue reading “Kalman Filter”

In loose terms, the mixing time is the amount of time to wait before you can expect a Markov chain to be close to its stationary distribution. We give an upper bound for this.

We consider a continuous time analogue of Markov Decision Processes.

The link below contains notes PDF for this years stochastic control course

I’ll upload individual posts for each section. I’ll likely update these notes and add more exercises over the coming semester. I’ll add this update in a further post at the end of the course. Comments, typos, suggestions are always welcome.