--- a/doc/v2_planning/committees.txt	Fri Sep 03 18:30:21 2010 -0400
+++ b/doc/v2_planning/committees.txt	Fri Sep 03 21:57:22 2010 -0400
@@ -2,11 +2,11 @@
 
 * Existing Python ML libraries investigation: GD, DWF, IG, DE
 * Dataset interface: DE, OB, OD, AB, PV
-* Learners: AB, PL, GM, IG, RP
+* Learners: AB, PL, GM, IG, RP, NB
 * Optimization: JB, PL, OD
 * Inference/sampling: JB, GD, AC
 * Job management, analysis, metrics, costs, visualization: GD, FS, PL, XM
-* Formulas/tags: FB, NB, RP, AC, OB
+* Formulas/tags: FB, RP, AC, OB
 * Coding style: DE, OD, DWF, FB
 
 Issues to be tackled in the future:

--- a/doc/v2_planning/optimization.txt	Fri Sep 03 18:30:21 2010 -0400
+++ b/doc/v2_planning/optimization.txt	Fri Sep 03 21:57:22 2010 -0400
@@ -30,8 +30,12 @@
  - sgd with annealing schedule
  - TONGA
  - James Marten's Hessian-free
+ - Conjugate gradients, batch and (large) mini-batch [that is also what Marten's thing does]
 
 Do we need anything to make batch algos work better with Pylearn things?
- - conjugate methods?
- - L-BFGS?
+ - conjugate methods? yes
+ - L-BFGS? maybe, when needed
 
+
+
+

--- a/doc/v2_planning/sampler.txt	Fri Sep 03 18:30:21 2010 -0400
+++ b/doc/v2_planning/sampler.txt	Fri Sep 03 21:57:22 2010 -0400
@@ -1,3 +1,6 @@
+
+Inference / Sampling committee: JB, GD, AC
+
 OVERVIEW
 ========
 
@@ -36,4 +39,12 @@
 * Annealing
 * Parallel Tempering, Tempered Transitions, Simulated Tempering
 * Nested Sampling (?)
-* Hamiltonian Monte Carlo
+* Hamiltonian Monte Carlo --> or is it Hybrid Monte Carlo?
+
+3. USAGE PATTERNS
+=================
+
+* MCMC methods have a usage pattern that is quite different from the kind of univariate sampling methods
+needed for nice-and-easy parametric families. 
+
+