Continuous Time Markov Chains
=============================
**Authors**: [Thomas J. Sargent](http://www.tomsargent.com/) and [John
Stachurski](https://johnstachurski.net/)
This lecture series provides a short introduction to the field of continuous
time Markov chains. Focus is shared between theory, applications and
computation. Mathematical ideas are combined with computer code to help
clarify and build intuition, as well as to bridge the gap between theory and
applications.
The presentation is relatively rigorous but the aim is towards applications
rather than mathematical curiosities (which are plentiful, if one starts to
look). Applications are drawn from economics, finance and operations
research.
```{admonition} Solved exercises
There are many solved exercises and we recommend readers attempt all of them,
or at least review the solutions.
```
```{admonition} Computer code
The code is written in Python and is accelerated through a combination of
[NumPy](https://numpy.org/) (vectorized code) and just-in-time compilation
(via [Numba](http://numba.pydata.org/)).
QuantEcon provides a fast-paced [introduction to scientific computing with Python](https://python-programming.quantecon.org/) that covers these topics.
```
```{admonition} Background: Markov chains in discrete time
The lectures are well suited to those who have some knowledge of discrete time
Markov chains and wish to learn more about their continuous time cousins. A
suitable preliminary discussion of discrete time Markov chains can be found
[here](https://python.quantecon.org/finite_markov.html).
```
```{admonition} Prerequisites: Probability and Analysis
Readers are assumed to be familiar with probability and a small amount of
analysis. Later lectures, which deal with infinite state spaces, assume that
require that readers are comfortable with the basics of linear analysis in Banach space.
```
The lectures are written using [Jupyter Book](https://jupyterbook.org/intro.html).