pachadotdev

# Option 1: Use apt-get

# keys taken from https://software.intel.com/en-us/articles/installing-intel-free-libs-and-python-apt-repo
cd ~/GitHub/r-with-intel-mkl/
wget https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS-2019.PUB
apt-key add GPG-PUB-KEY-INTEL-SW-PRODUCTS-2019.PUB

sudo sh -c 'echo deb https://apt.repos.intel.com/mkl all main > /etc/apt/sources.list.d/intel-mkl.list'
sudo apt-get update && sudo apt-get install intel-mkl-64bit

lirnli

delameko

TL;DR

Install Postgres 9.6, and then:

sudo pg_dropcluster 9.6 main --stop
sudo pg_upgradecluster 9.5 main
sudo pg_dropcluster 9.5 main

mikekaminsky

CREATE OR REPLACE FUNCTION kaplan_meier(text)
RETURNS TABLE (
  time_period int,
  pivot text,
  exposed int,
  events int,
  marginal_probability double precision,
  cumulative_probability double precision
) AS 
$PROC$

josef-pkt

outro56

1. *General Background and Overview*
  * [Probabilistic Data Structures for Web Analytics and Data Mining](http://highlyscalable.wordpress.com/2012/05/01/probabilistic-structures-web-analytics-data-mining/) : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
  * [Models and Issues in Data Stream Systems](http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.106.9846)
  * [Philippe Flajolet’s contribution to streaming algorithms](https://speakerdeck.com/timonk/philippe-flajolets-contribution-to-streaming-algorithms) : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
  * [Approximate Frequency Counts over Data Streams](http://www.vldb.org/conf/2002/S10P03.pdf) by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
  * [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep