misc

jstac · jstac · commit 919cc8f35adf · 2021-08-06T09:19:17.000+10:00
diff --git a/ctmc_lectures/intro.md b/ctmc_lectures/intro.md
@@ -4,52 +4,20 @@ 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.
+These lectures provides a short introduction to continuous time Markov chains.
+Mathematical ideas are combined with computer code to build intuition and
+bridge the gap between theory and applications.  There are many solved
+exercises.
 
-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.
+The presentation is rigorous but aims toward applications rather than
+mathematical curiosities (which are plentiful, if one starts to look).
+Applications are drawn from economics, finance and operations research.  I
+assume readers have some knowledge of [discrete time Markov
+chains](https://python.quantecon.org/finite_markov.html).  Later lectures, use
+a small amount of analysis in Banach space.
 
+Code is written in Python and accelerated using
+JIT compilation via [Numba](http://numba.pydata.org/).  QuantEcon provides an
+[introduction to these topics](https://python-programming.quantecon.org/).
 
-```{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).
