Bayesian Higgs combination based on event counts (follow-up from 11 May 07)

1. Bayesian Higgs combination based on event counts (follow-up from 11 May 07) ATLAS Statistics Forum CERN, 3 September, 2007 Glen Cowan Physics Department Royal Holloway, University of London g.cowan@rhul.ac.uk www.pp.rhul.ac.uk/~cowan Bayesian Higgs combination

2. Outline 0 Quick recap of some formalism (see also 11 May 07 talk) 1 Markov Chain Monte Carlo for Bayesian computation 2 Computing Bayes factors 3 Application to ATLAS Higgs combination Bayesian Higgs combination

3. Recap of Bayesian approach Bayes’ theorem tells how our beliefs in a hypothesis (e.g. a parameter q) should be updated in light of the data x: prior likelihood posterior E.g. for intervals/limits, integrate posterior pdf p(q | x) to have desired probability content. 95% CL upper limit from Bayesian Higgs combination

4. Marginalization with MCMC Bayesian computations involve integrals like often high dimensionality and impossible in closed form, also impossible with ‘normal’ acceptance-rejection Monte Carlo. Markov Chain Monte Carlo (MCMC) has revolutionized Bayesian computation. Google for ‘MCMC’, ‘Metropolis’, ‘Bayesian computation’, ... MCMC generates correlated sequence of random numbers: cannot use for many applications, e.g., detector MC; effective stat. error greater than √n . Basic idea: sample multidimensional look, e.g., only at distribution of parameters of interest. Bayesian Higgs combination

5. MCMC basics: Metropolis-Hastings algorithm Goal: given an n-dimensional pdf generate a sequence of points Proposal density e.g. Gaussian centred about 1) Start at some point 2) Generate 3) Form Hastings test ratio 4) Generate move to proposed point 5) If else old point repeated 6) Iterate Bayesian Higgs combination

6. Metropolis-Hastings (continued) This rule produces a correlated sequence of points (note how each new point depends on the previous one). For our purposes this correlation is not fatal, but statistical errors larger than naive The proposal density can be (almost) anything, but choose so as to minimize autocorrelation. Often take proposal density symmetric: Test ratio is (Metropolis-Hastings): I.e. if the proposed step is to a point of higher , take it; if not, only take the step with probability If proposed step rejected, hop in place. Bayesian Higgs combination

7. Metropolis-Hastings caveats Actually one can only prove that the sequence of points follows the desired pdf in the limit where it runs forever. There may be a “burn-in” period where the sequence does not initially follow Unfortunately there are few useful theorems to tell us when the sequence has converged. Look at trace plots, autocorrelation. Check result with different proposal density. If you think it’s converged, try it again with 10 times more points. Bayesian Higgs combination

8. Example: posterior pdf from MCMC Summarize pdf of parameter of interest with, e.g., mean, median, standard deviation, etc. Bayesian Higgs combination

9. Bayesian model selection (‘discovery’) The probability of hypothesis H0 relative to its complementary alternative H1 is often given by the posterior odds: no Higgs Higgs prior odds Bayes factor B01 The Bayes factor is regarded as measuring the weight of evidence of the data in support of H0 over H1. Interchangeably use B10 = 1/B01 Bayesian Higgs combination

10. Assessing Bayes factors One can use the Bayes factor much like a p-value (or Z value). There is an “established” scale, analogous to our 5s rule: B10 Evidence against H0 -------------------------------------------- 1 to 3 Not worth more than a bare mention 3 to 20 Positive 20 to 150 Strong > 150 Very strong Kass and Raftery, Bayes Factors, J. Am Stat. Assoc 90 (1995) 773. 11 May 07: Not clear how useful this scale is for HEP. 3 Sept 07: Upon reflection & PHYSTAT07 discussion, seems like an intuitively useful complement to p-value. Bayesian Higgs combination

11. Rewriting the Bayes factor Suppose we have models Hi, i = 0, 1, ..., each with a likelihood and a prior pdf for its internal parameters so that the full prior is where is the overall prior probability for Hi. The Bayes factor comparing Hi and Hj can be written Bayesian Higgs combination

12. Bayes factors independent of P(Hi) For Bij we need the posterior probabilities marginalized over all of the internal parameters of the models: Use Bayes theorem Ratio of marginal likelihoods So therefore the Bayes factor is The prior probabilities pi = P(Hi) cancel. Bayesian Higgs combination

13. Numerical determination of Bayes factors Both numerator and denominator of Bij are of the form ‘marginal likelihood’ Various ways to compute these, e.g., using sampling of the posterior pdf (which we can do with MCMC). E.g., consider only one model and write Bayes theorem as: Bayesian Higgs combination

14. Harmonic mean estimator E.g., consider only one model and write Bayes theorem as: p(q) is normalized to unity so integrate both sides, posterior expectation Therefore sample q from the posterior via MCMC and estimate m with one over the average of 1/L (the harmonic mean of L). Bayesian Higgs combination

15. Improvements to harmonic mean estimator The harmonic mean estimator is numerically very unstable; formally infinite variance (!). Gelfand & Dey propose variant: Rearrange Bayes thm; multiply both sides by arbitrary pdf f(q): Integrate over q : Improved convergence if tails of f(q) fall off faster than L(x|q)p(q) Note harmonic mean estimator is special case f(q) = p(q). . Bayesian Higgs combination

16. Importance sampling Need pdf f(q) which we can evaluate at arbitrary q and also sample with MC. The marginal likelihood can be written Best convergence when f(q) approximates shape of L(x|q)p(q). Use for f(q) e.g. multivariate Gaussian with mean and covariance estimated from posterior (e.g. with MINUIT). Bayesian Higgs combination

17. Bayes factor computation discussion Also tried method of parallel tempering; see note and also Harmonic mean OK for very rough estimate. I had trouble with all of the methods based on posterior sampling. Importance sampling worked best, but may not scale well to higher dimensions. Lots of discussion of this problem in the literature, e.g., Bayesian Higgs combination

18. Bayesian Higgs analysis N independent channels, count ni events in search regions: Constrain expected background bi with sideband measurements: Expected number of signal events: (m is global parameter, m = 1 for SM). Consider a fixed Higgs mass and assume SM branching ratios Bi. Suggested method: constrain m with limit mup; consider mH excluded if upper limit mup < 1.0. For discovery, compute Bayes factor for H0 : m = 0 vs. H1 : m = 1 Bayesian Higgs combination

19. Parameters of Higgs analysis E.g. combine cross section, branching ratio, luminosity, efficiency into a single factor f: Systematics in any of the factors can be described by a prior for f, use e.g. Gamma distribution. For now ignore correlations, but these would be present e.g. for luminosity error: ai, bi from nominal value fi,0 and relative error ri=sf,i / fi,0 : Bayesian Higgs combination

20. Bayes factors for Higgs analysis The Bayes factor B10 is Compute this using a fixed m for H1, i.e., pm(m) = d(m-m′), then do this as a function of m′. Look in particular at m = 1. Take numbers from VBF paper for 10 fb-1, mH = 130 GeV: lnjj was for 30 fb-1, in paper; divided by 3 Bayesian Higgs combination

21. Bayes factors for Higgs analysis: results (1) Create data set by hand with ni ~ nearest integer (fi + bi), i.e., m = 1: n1 =22, n2 =22, n3 = 4. For the sideband measurements mi, choose desired sb/b, use this to set size of sideband (i.e. sb/b = 0.1 → m = 100). B01 for sf/f = 0.1, different values of sb/b., as a function of m. Bayesian Higgs combination

22. Bayes factors for Higgs analysis: results (2) B01 for sb/b = 0.1, different values of sf/f, as a function of m. Effect of uncertainty in fi (e.g., in the efficiency): m = 1 no longer gives a fixed si, but a smeared out distribution. →lower peak value of B10. Bayesian Higgs combination

23. Bayes factors for Higgs analysis: results (3) Or try data set with ni ~ nearest integer bi, i.e., m = 0: n1 =9, n2 =10, n3 = 2. Used sb/b = 0.1, sf/f, = 0.1. Here the SM m = 1 is clearly disfavoured, so we set a limit on m. Bayesian Higgs combination

24. Posterior pdf for m , upper limits (1) Here done with (improper) uniform prior, m > 0. (Can/should also vary prior.) Bayesian Higgs combination

25. Posterior pdf for m , upper limits (2) Bayesian Higgs combination

26. Summary, outlook, etc. There is a draft note describing this on www.pp.rhul.ac.uk/~cowan/atlas/higgs_bayes.ps, pdf Need to think more about how to calibrate Bayes factors relative to p-values, i.e., study sampling distribution of B10. Advantage of Bayesian method, especially with MCMC, is that one can consider many possible forms for the systematic error, change the tails, consider “error on the error”, etc. Done so far for 3 channels; need to see how this scales for many more. Bayesian Higgs combination