# HG changeset patch
# User gdesjardins
# Date 1283540483 14400
# Node ID 91916536a3046ee5bfc22f4b2bd1decb5e5c94a8
# Parent  790376d986a37a69de3e71201b7db7936e63569f# Parent  af80b7d182af3746a12c602c66ac37dbd9c3a4c2
merge

diff -r af80b7d182af -r 91916536a304 doc/v2_planning/sampler.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/doc/v2_planning/sampler.txt	Fri Sep 03 15:01:23 2010 -0400
@@ -0,0 +1,39 @@
+OVERVIEW
+========
+
+Before we start defining what a sampler is and how it should be defined in
+pylearn, we should first know what we're up against. 
+
+The workflow I have in mind is the following:
+1. identify the most popular sampling algorithms in the litterature
+2. get up to speed with methods we're not familiar with
+3. identify common usage patterns, properties of the algorithm, etc.
+4. decide on an API / best way to implement them
+5. prioritize the algorithms
+6. code away
+
+1.BACKGROUND
+=============
+
+This section should provide a brief overview of what exists in the litterature.
+We should make sure to have a decent understanding of all of these (not everyone
+has to be experts though), so that we can *intelligently* design our sampler
+interface based on common usage patterns, properties, etc.
+
+Sampling from basic distributions
+* already supported: uniform, normal, binomial, multinomial
+* wish list: beta, poisson, others ?
+
+List of sampling algorithms:
+
+* inversion sampling
+* rejection sampling
+* importance sampling
+* Markov Chain Monte Carlo 
+* Gibbs sampling
+* Metropolis Hastings
+* Slice Sampling
+* Annealing
+* Parallel Tempering, Tempered Transitions, Simulated Tempering
+* Nested Sampling (?)
+* Hamiltonian Monte Carlo