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@jalavik
jalavik / unicode_to_latex.py
Created May 17, 2011 11:04 — forked from beniwohli/unicode_to_latex.py
Map to convert unicode characters to their respective LaTeX representation
# original XML at http://www.w3.org/Math/characters/unicode.xml
# XSL for conversion: https://gist.github.com/798546
unicode_to_latex = {
u"\u0020": "\\space ",
u"\u0023": "\\#",
u"\u0024": "\\textdollar ",
u"\u0025": "\\%",
u"\u0026": "\\&",
#include <iostream>
#include <hdf5.h>
// Constants
const char saveFilePath[] = "test.h5";
const hsize_t ndims = 2;
const hsize_t ncols = 3;
int main()
@chengyin
chengyin / linkedout.js
Last active July 11, 2021 15:23
Unsubscribe all LinkedIn email in "one click". For an easier to use version, you can check out the bookmarklet: http://chengyin.github.io/linkedin-unsubscribed/
// 1. Go to page https://www.linkedin.com/settings/email-frequency
// 2. You may need to login
// 3. Open JS console
// ([How to?](http://webmasters.stackexchange.com/questions/8525/how-to-open-the-javascript-console-in-different-browsers))
// 4. Copy the following code in and execute
// 5. No more emails
//
// Bookmarklet version:
// http://chengyin.github.io/linkedin-unsubscribed/
@martijnvermaat
martijnvermaat / ssh-agent-forwarding-screen.md
Created December 21, 2013 15:06
SSH agent forwarding and screen

SSH agent forwarding and screen

When connecting to a remote server via SSH it is often convenient to use SSH agent forwarding so that you don't need a separate keypair on that server for connecting to further servers.

This is enabled by adding the

ForwardAgent yes

option to any of your Host entries in ~/.ssh/config (or alternatively with the -A option). Don't set this option in a wildcard Host * section since any user on the remote server that can bypass file permissions can now als use keys loaded in your SSH agent. So only use this with hosts you trust.

Horizons in Probabilistic Programming and Bayesian Analysis

Representations:

  • Hierarchical models
  • Hidden Markov models
  • Graphical models
  • Non-parametric Bayes (distributions over functions)

Inference Approaches:

segments = 5
accels = [ 0, 1, 2, 3, 4 ]
vs = [ 0, 0.5, 1, 1.5, 2 ]
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
ts = [ np.linspace(t * 100, t * 100 + 99, 10) for t in range(5)]