1. Statistical Tools In Dzero PHYSTAT Workshop 2005 Harrison B. Prosper1 Statistical Software In DØ The Good, the Bad and the Non-Existent Harrison B. Prosper Florida State University PHYSTAT Workshop 2005 15 August 2005

2. Statistical Tools In Dzero PHYSTAT Workshop 2005 Harrison B. Prosper2 Outline  Analysis Example  Available Software  Wish List  Summary

3. Statistical Tools In Dzero PHYSTAT Workshop 2005 Harrison B. Prosper3 Example - DØ Single Top Group – I

4. Statistical Tools In Dzero PHYSTAT Workshop 2005 Harrison B. Prosper4 Example - DØ Single Top Group – II  Search for p+pbar → t + (q) + b + X  8 signal channels  7 background sources per signal channel  QCD, ttbar(lj), ttbar(ll), Wjj, Wbb, WW, WZ  Each data bin is the sum of  tb, tqb, QCD, ttbar(lj), ttbar(ll), Wjj, Wbb, WW, WZ

5. Statistical Tools In Dzero PHYSTAT Workshop 2005 Harrison B. Prosper5 Example - DØ Single Top Group – III  Basic Statistical Quantity: Binned Likelihood  Goal  To measure  s and  t

6. Statistical Tools In Dzero PHYSTAT Workshop 2005 Harrison B. Prosper6 Example – Statistical Problems – IV  Background Modeling  Model/data comparisons in multiple dimensions to determine region with “best” match.  Background events are generally weighted, for example, by the probability that it could contain a b-jet.  These “tag-rate functions” are the results of fits to 2 – 3 dimensional empirical densities.

7. Statistical Tools In Dzero PHYSTAT Workshop 2005 Harrison B. Prosper7 Example – Statistical Problems – V  Discriminant Variable Selection  From a list of potentially useful variables, select the “best” sub-set.  Multivariate Analyses  Random Grid Search  Neural Networks  Decision Trees  Bayesian Neural Networks

8. Statistical Tools In Dzero PHYSTAT Workshop 2005 Harrison B. Prosper8 Example – Statistical Problems – VI  Posterior Density Computation  Must marginalize over hundreds of variables (acceptances and background yields) and must do so taking into account known dependencies.  Analysis Validation  Ideally, the entire analysis is run repeatedly on fake data-sets to study its frequency behavior.

9. Statistical Tools In Dzero PHYSTAT Workshop 2005 Harrison B. Prosper9 Available Software – I  Fitting  Minuit applied to Root histograms  PoissonGammaFit (more later!)  Multivariate Methods  RGSearch(a few incompatible versions)  Jetnet (v3.4) (with C++ binding  MLPfit(several versions)  oo_neural (OOP version of BP)

10. Statistical Tools In Dzero PHYSTAT Workshop 2005 Harrison B. Prosper10 Available Software – II  Classifier (decision tree)  C2.4 (decision tree)  TerraFerma (misc. methods)  BNN(Bayesian NN)  Limit Setting  top_statistics(Bayes, CLs)  blimit(Bayes – more robust version of DØ web-calculator)

11. Statistical Tools In Dzero PHYSTAT Workshop 2005 Harrison B. Prosper11 Available Software – III  Adaptive Numerical Integration  AdBayes(C++ binding of Alan Genz’s Fortran code)  Python Bindings  RGSearch, Jetnet, AdBayes, PoissonGammaFit, CLHEP, Coin, Root, etc.

12. Statistical Tools In Dzero PHYSTAT Workshop 2005 Harrison B. Prosper12 PoissonGammaFit  Model  For each bin i we write the (mean) data count d i as a linear sum of N (mean) source counts  Likelihood for observed distribution D ={D i }

13. Statistical Tools In Dzero PHYSTAT Workshop 2005 Harrison B. Prosper13 PoissonGammaFit – II  Bayesian Inference for Moments m r of p  Prior (given source counts A ji )

14. Statistical Tools In Dzero PHYSTAT Workshop 2005 Harrison B. Prosper14 What’s Available?  C++ Class  PoissonGammaFit (vvdouble& A, vdouble&D, stringprior=“flat”, bool scale=true, inttotal=10000)  Methods  m = o.mean()  v = o.variance() vdouble= vector vvdouble = vector >

15. Statistical Tools In Dzero PHYSTAT Workshop 2005 Harrison B. Prosper15 What’s Available? – II  Main Program Usage: pgammafit-h -f [hist-file-list (histfile.list)] -n [# of sampling points (10000)] -o [name of plot (pgammafit.gif)]  Uses  HistogramCache, PoissonGammaFit, Minuit

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17. Statistical Tools In Dzero PHYSTAT Workshop 2005 Harrison B. Prosper17 Bayesian Neural Networks y(x,w) x1x1 x2x2 u, a v, b w = (u, a, v, b) weights For binary (0,1) classification p(1|x) y(x,w) → p(1|x)

18. Statistical Tools In Dzero PHYSTAT Workshop 2005 Harrison B. Prosper18 HT_AllJets_MinusBestJets Dots p(1|H T ) = H tqb /(H tqb +H Wbb ) H is a 1-D histogram Curves individual NNs y(H T, w n ) Black curve Bayesian Neural Networks – II

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20. Statistical Tools In Dzero PHYSTAT Workshop 2005 Harrison B. Prosper20 What’s Available?  Radford Neal’s Package  C-codes compiled and linked into a set of programs:  net-specSpecify network  data-specSpecify training data  net-genInitialize network  mc-specSpecify MCMC parameters  net-mcRun MCMC  net-displayDisplay network parameters  netwrite.pyWrite results to a C++ function

21. Statistical Tools In Dzero PHYSTAT Workshop 2005 Harrison B. Prosper21 The Bad  It’s a Jungle Out There!  Difficult to express ideas clearly  Tools typically cannot be moved, easily, from one framework to another  No clear protocol for interface between heterogeneous data formats  No algebra of histograms  Histograms tightly coupled to their viewers: Use Root or die!

22. Statistical Tools In Dzero PHYSTAT Workshop 2005 Harrison B. Prosper22 The Bad – II  Inadequate Support For:  Generating ensembles of observations, possibly with conditioning, to study bias, variance, coverage etc.  Assessing robustness with respect to likelihoods and prior densities  Studying different confidence limit procedures  Studying different optimization criteria

23. Statistical Tools In Dzero PHYSTAT Workshop 2005 Harrison B. Prosper23 The Non-Existent  DØ has no (or inadequate) tools to:  Browse data in truly interesting ways  Perform goodness-of-fit tests that go beyond KS and χ 2  Construct Bayesian models, systematically  Perform sensitivity analyses, systematically  No domain-specific language

24. Statistical Tools In Dzero PHYSTAT Workshop 2005 Harrison B. Prosper24 Wish List – I  Free At Last!  Statistical tool separate from, and independent of, the environment in which it might be used.  However, provide bindings for different environments/languages (R, Root, Ruby, Python, Java, etc.)  Less Is More!  Each statistical tool should encapsulate a single coherent statistical idea.

25. Statistical Tools In Dzero PHYSTAT Workshop 2005 Harrison B. Prosper25 Wish List – II  Histograms  Histogram and histogram viewers should be 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

26. Statistical Tools In Dzero PHYSTAT Workshop 2005 Harrison B. Prosper26 Wish List – III  Likelihoods  Flexible method for reporting them; perhaps as swarms of points generated via MCMC?  Frequency Methods  Flexible ensemble generator, with easily extracted sub-ensembles  Flexible query of ensembles (to get coverage, error rates, variances, bias etc.)

27. Statistical Tools In Dzero PHYSTAT Workshop 2005 Harrison B. Prosper27 Wish List – IV  Bayesian Methods  Flexible robustness studies (prior family, likelihood family etc.)  Multi-dimensional integration (adaptive and Markov Chain MC)  Domain Specific Language  No dereferencing, auto_ptr, dynamic_cast, pointers, templates etc. please,… we’re British!

28. Statistical Tools In Dzero PHYSTAT Workshop 2005 Harrison B. Prosper28 Summary  The Good  Many statistical tools are in use at DØ  A lot more needed – opportunity for creativity!  The Bad  Current tools are a reflection of non-interacting idiosyncratic minds!  The Non-Existent  Lack of a domain-specific language for expression of statistical ideas. I don’t want to think about pointers and const-correctness when I’m trying to think about mathematics.