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MAINT: remove matplotlib inline #392

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Apr 26, 2024
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MAINT: remove matplotlib inline #392

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7d6aa4d
MAINT: remove matplotlib inline
mmcky Apr 26, 2024
a74770e
fix linkchecker issue
mmcky Apr 26, 2024
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8 changes: 8 additions & 0 deletions lectures/_static/quant-econ.bib
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Expand Up @@ -3,6 +3,14 @@
Note: Extended Information (like abstracts, doi, url's etc.) can be found in quant-econ-extendedinfo.bib file in _static/
###

@book{Brunton_Kutz_2019,
place={Cambridge},
title={Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control},
publisher={Cambridge University Press},
author={Brunton, Steven L. and Kutz, J. Nathan},
year={2019}
}

@article{wallis1980statistical,
title={The statistical research group, 1942--1945},
author={Wallis, W Allen},
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3 changes: 0 additions & 3 deletions lectures/kalman_2.md
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Expand Up @@ -51,15 +51,12 @@ In addition to what's in Anaconda, this lecture will need the following librarie
To conduct simulations, we bring in these imports, as in {doc}`A First Look at the Kalman filter <kalman>`.

```{code-cell} ipython3
%matplotlib inline
import matplotlib.pyplot as plt
plt.rcParams["figure.figsize"] = (11, 5) #set default figure size
import numpy as np
from quantecon import Kalman, LinearStateSpace
from collections import namedtuple
from scipy.stats import multivariate_normal
import matplotlib as mpl

mpl.rcParams['text.usetex'] = True
mpl.rcParams['text.latex.preamble'] = r'\usepackage{{amsmath}}'
```
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2 changes: 1 addition & 1 deletion lectures/var_dmd.md
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Expand Up @@ -269,7 +269,7 @@ Here an $ m \times n+1 $ data matrix $ \tilde X $ contains many more attributes

Dynamic mode decomposition was introduced by {cite}`schmid2010`,

You can read about Dynamic Mode Decomposition here {cite}`DMD_book` and here [[BK19](https://python.quantecon.org/zreferences.html#id25)] (section 7.2).
You can read about Dynamic Mode Decomposition {cite}`DMD_book` and {cite}`Brunton_Kutz_2019` (section 7.2).

**Dynamic Mode Decomposition** (DMD) computes a rank $ r < p $ approximation to the least squares regression coefficients $ \hat A $ described by formula {eq}`eq:AhatSVDformula`.

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