jkeirstead

## Analysis of DECC NEED data
## James Keirstead
## 22 November 2012
##
## Underlying data available from
##     http://www.decc.gov.uk/en/content/cms/statistics/energy_stats/en_effic_stats/need/need.aspx
## For more info, see
##     http://www.jameskeirstead.ca/blog/we-need-more-data/ 

jkeirstead

# Demo of Gaussian process regression with R
# James Keirstead
# 5 April 2012

# Chapter 2 of Rasmussen and Williams's book `Gaussian Processes
# for Machine Learning' provides a detailed explanation of the
# math for Gaussian process regression.  It doesn't provide
# much in the way of code though.  This Gist is a brief demo
# of the basic elements of Gaussian process regression, as
# described on pages 13 to 16.

jkeirstead

# Generate a Monte Carlo sample using Sobol' low-discrepancy quasi-random sequences
# James Keirstead
# 3 February 2012
#
# Random sampling with R's standard methods is inefficient for Monte Carlo analysis as
# the sampled values do not cover the parameter space evenly.  This Gist allows users
# to create parameter samples using Sobol' sequences to get around this problem.

# makeMCSample
#    Makes a Monte Carlo sample using Sobol' sequences