Statistical Tools PhyStat Workshop 2004 Harrison B. Prosper1 Statistical Tools A Few Comments Harrison B. Prosper Florida State University PHYSTAT Workshop.

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Presentation transcript:

Statistical Tools PhyStat Workshop 2004 Harrison B. Prosper1 Statistical Tools A Few Comments Harrison B. Prosper Florida State University PHYSTAT Workshop March 2004

Statistical Tools PhyStat Workshop 2004 Harrison B. Prosper2 Outline  Issues  Wish List  Example  Summary

Statistical Tools PhyStat Workshop 2004 Harrison B. Prosper3 Statistical Tools: Issues  Some difficulties with tools used in HEP  Difficult to express ideas cleanly and clearly  Tools scattered over different (typically, monolithic) programs  Interface between heterogeneous data formats and disparate tools is a headache  Histograms are tightly coupled to their viewers  Algebra of histograms relatively crude  Inadequate support for systematic study of ensembles

Statistical Tools PhyStat Workshop 2004 Harrison B. Prosper4 Issues – II  In a systematic statistical study one may wish to:  Generate different ensembles of observations, possibly with conditioning, and study various statistical properties (bias, variance, coverage etc.)  Assess robustness with respect to  prior densities and likelihoods  Study different confidence limit procedures  Study different optimization criteria

Statistical Tools PhyStat Workshop 2004 Harrison B. Prosper5 Issues – III  One may wish to study:  Type I and type II error rates  Consistency – both convergence to, and rate of convergence to, the true answer as sample size increases  Probability densities p(z) given underlying distributions p(x)

Statistical Tools PhyStat Workshop 2004 Harrison B. Prosper6 Wish List  Decoupling  Statistical tool separate from, and independent of, the environment in which it might be used.  However, provide bindings for different environments/languages (R, Root, Python, Java, etc.)  Modularity  Each statistical tool encapsulates a single coherent statistical idea. Avoid monoliths.  Histograms  Histogram and histogram viewers independent of each other. (A sensible idea from Marc Paterno!)  Elegant algebra of histograms h = a*h 1 +b*h 2 /h 3 etc.  Powerful, intuitive tools for multi-dim. data exploration

Statistical Tools PhyStat Workshop 2004 Harrison B. Prosper7 Wish List – II  Likelihoods  Flexible method for reporting them; maybe as swarms of points generated via MCMC?  Frequency Methods  Flexible ensemble generator, which allows easily extracted sub-ensembles  Flexible query of ensembles (to get coverage, error rates, variances, bias etc.)  Bayesian Methods  Flexible robustness studies (prior family, likelihood family etc.)  Multi-dimensional integration (adaptive and Markov chain MC)

Statistical Tools PhyStat Workshop 2004 Harrison B. Prosper8 Example: A Current Statistical Problem From DØ Single Top Group  Set limit on  (p+pbar → t + X) given an histogram for each of  4 signal channels  tq(EC), tqb(EC), tq(CC), tqb(CC)  4 background sources per signal channel  QCD, ttbar(l+jets), ttbar(ll), W+Jets  Some histograms are weighted, some unweighted  We would like to study different limit procedures, including Bayesian, and study their frequency properties. Currently using ad hoc and rather inflexible pieces of homegrown C++!

Statistical Tools PhyStat Workshop 2004 Harrison B. Prosper9 Summary  The Good  Lots of statistical tools already exist  A lot more needed – opportunity for creativity!  The Bad  Use of current tools, however, often requires familiarity with several frameworks/languages  The Ugly  Lack of a simple, but powerful, language for expression of statistical ideas. Rapid “what if” analyses done with C++. This is crazy! I don’t want to think about pointers and de-referencing when I’m trying to think about mathematics.