[notebook]
Bootstrap for uncertainty in CSMF estimation
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One more IDV in Python approach
https://plot.ly/dash/
https://community.plot.ly/c/dash
https://github.com/plotly/dash
https://plot.ly/dash/getting-started
View at Medium.com
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Its 2018, how to IDV in Python?
I’ve got a fun little viz that I need to demo for Important People (IP) in early March [editor’s note: still not done… that deadline was highly optimistic!]. How to do it?
In Python? Sure. In a Jupyter notebook? Maybe. With Matplotlib? Probably not… at least I better have a look at the state of the alternatives.
Did I mention that it is essential for this viz to be *interactive*? It needs to allow the Important People to explore the predictions of some ML model, or at least allow me to explore them while they call out how to explore.
Years ago, I attempted to designate a particular plot the “hello, world” of data viz. Remember that? I think we should extend it to a hello world of interactive data viz. Maybe just choosing the number of digits is enough. Or should it follow the visual information seeking mantra? But “hello, world” cannot be too complicated.
yhat?
Altair
https://altair-viz.github.io
https://github.com/altair-viz/altair_widgets/blob/master/examples/Iris.ipynb
http://pbpython.com/altair-intro.html
Bokeh
https://bokeh.pydata.org/en/latest/docs/gallery.html#gallery
https://www.analyticsvidhya.com/blog/2015/08/interactive-data-visualization-library-python-bokeh/
https://www.datacamp.com/courses/interactive-data-visualization-with-bokeh/
https://www.datacamp.com/community/blog/bokeh-cheat-sheet-python
https://demo.bokehplots.com/apps/movies
A Dramatic Tour through Python’s Data Visualization Landscape (including ggplot and Altair)
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Schelling’s Segregation Model
Thinking about this since I saw a gentrification talk at CSSS last week [editor’s note: more like a year ago]. Also thinking about engaging demo material for vivarium. Here are some recent links:
https://arxiv.org/abs/1705.03213
http://nifty.stanford.edu/2014/mccown-schelling-model-segregation/
http://jasss.soc.surrey.ac.uk/15/1/6.html
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G.E.P. Box on Model Building
The Edward library http://edwardlib.org/ has made the modeling approach of G.E.P. Box sound appealing to me. Here is some reading on it:
https://www.jstor.org/stable/1266570?seq=1#page_scan_tab_contents
https://www.jstor.org/stable/1266125?seq=1#page_scan_tab_contents
https://www.jstor.org/stable/1266318?seq=1#page_scan_tab_contents
https://www.jstor.org/stable/2286841?seq=1#page_scan_tab_contents
https://www.jstor.org/stable/2982063?seq=1#page_scan_tab_contents
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NYT on Philosophy of Science
Remember that AI class I’ve been blogging about? It’s got me thinking. Here is a take on what is collaboration that I would not have noticed without the priming from our discussion a couple week’s ago: “In a formulation that was galling to both sociologists and scientists, [Latour] once argued that Louis Pasteur did not just, as is commonly accepted, discover microbes; rather, he collaborated with them.”
From https://www.nytimes.com/2018/10/25/magazine/bruno-latour-post-truth-philosopher-science.html
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Typology of model users
From same article as above https://onlinelibrary.wiley.com/doi/epdf/10.1002/sdr.1596
Champions
Skeptics
Mystics
Social organizational network as an important factor as well— power mapping with Spectrum of Allies exercise useful?
“[H]aving a technically sound model is not enough to assure widespread and effective use …” — additional elements, such as a champion in a leadership role and clearly defined problem to model help.
A goal that I have not yet attained: finding a keeper-of-the-model in the client organization, who can run additional scenarios.
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