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## Zoomable Treemap

Treemaps for visualizing hierarchical data. Click to zoom to the next level.
Click on the top orange band to zoom out. Based on Mike Bostock's
[Zoomable Treemaps](http://bost.ocks.org/mike/treemap/)

This template follows [pigshell](http://pigshell.com)'s convention for "gist
templates":

  * It is supplied data using postMessage(), as a single object of the form

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function Gauge(placeholderName, configuration)
{
	this.placeholderName = placeholderName;
	
	var self = this; // for internal d3 functions
	
	this.configure = function(configuration)
	{
		this.config = configuration;
		

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This code is released under the MIT license.

Copyright (C) 2012 Matt Magoffin

Permission is hereby granted, free of charge, to any person obtaining a
copy of this software and associated documentation files (the
"Software"), to deal in the Software without restriction, including
without limitation the rights to use, copy, modify, merge, publish,
distribute, sublicense, and/or sell copies of the Software, and to
permit persons to whom the Software is furnished to do so, subject to

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license: gpl-3.0

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license: gpl-3.0
height: 600

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Agriculture as a percentage of GDP aganist industry as a percentage of GDP for
119 countries. Data from [Gapminder](http://www.gapminder.org/). Illustrates
brushing and linking using Vega's signals and predicates.

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Icons into Words in 3D using Three.js

The Time Remap Effect using Three.js and TweenMax.

If you want more technical info, please check this article. Sorry in only Japanese. http://ics-web.jp/lab/archives/7162

Usage Library

• Three.js r71

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*** Forked from the original by Stephen A Thomas (sathomnas) ***

Thanks to the excellent [bayes.js](http://www.sumsar.net/blog/2015/12/bayes-js-a-small-library-for-doing-mcmc-in-the-browser/) library from Rasmus Bååth it's now possible to experiment with Bayesian statistics in JavaScript. We'll take advantage of that library in this series of posts, which demonstrate Bayesian statistics visually for anyone with a web browser.

This first post covers Markov Chain Monte Carlo (MCMC), algorithms which are fundamental to modern Bayesian analysis. MCMC is also critical to many machine learning applications. Since this is the first post, though, we'll start with a brief introduction to Bayesian statistics.

## Bayes' Rule in Probability

The simplest form of Bayesian analysis arises in probability, not statistics, in the form of _Bayes' Rule_ (or _Bayes' Theorem_). Bayes' Rule describes relationships between conditional probabilities. Formally, it says:

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This graph shows the top 25 jazz albums of all time (at least according to [one blogger](http://www.thejazzresource.com/top_25_jazz_albums.html).) Links between the albums represent musicians that played on both. Click on the nodes and the links for more information.

> Note: iTunes links are _not_ affiliate links.

This visualization is a real application of the [D3.js](http://d3js.org) [force layout](https://github.com/mbostock/d3/wiki/Force-Layout). It demonstrates some of the principles in a series on that layout. You can review that series beginning with the [first example](http://jsdatav.is/visuals.html?id=11550728).

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This is part of a series of examples that describe the basic operation of 
the [D3.js](http://d3js.org) [force layout](https://github.com/mbostock/d3/wiki/Force-Layout).
Eventually they may end up in a blog post that wraps everything together.
If you missed the beginning of the series, here's a link to 
[first example](http://jsdatav.is/visuals.html?id=11550728).

The final parameter this series considers is `friction`. This parameter, which
ranges from 0 to 1, tells D3.js how much (if at all) to slow down the layout
at each iteration. With a value of 1, the layout never slows down, while a
value of 0 forces all the nodes to immediately stop.