Introduction:
Discussion on the general properties of various methods and comparison of their properties. Develop a set of desired properties. (Luc and Joel and David)

Barlow, Roger:
Significance and Likelihood ratio and confidence limits

Davison, Anthony:
p-value functions

Junk, Tom:
P-values and nuisance parameters

Burnett, Toby:
Detection of gamma rays

Bueno, James:
Bayesian upper limits

Demortier, Luc:
Reference analysis

Marchand, Eric:
Bayesian credible intervals and coverage

Zech, Gunter:
Likelihood vs coverage

Punzi, Giovanni:
Frequentist limits

Conrad, Jan and Cranmer, Kyle:
Profile likelihood for marked Poisson

Sen, Bodhisattva:
A Confidence intervals with nuisance parameters

Introduction:
general discussion and organization
(Byron and Nancy)

Bailey, Stephen
support vector machines

Neal, Radford:
Bayesian neural nets + robust classifiers

Toby Burnett:
Decision trees

Narsky, Ilya:
Multivariate classification

Blobel, Volker:
Systematics for goodness of fit

Meinshausen, Nicolai:
Using R for classification problems

LePage, Raoul:
Comments on Classification

Neal, Ranford:
Handling Systematic Errors in Simulations

Schwienhorst, Reinhard:
Multivariate classification Comparisons