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Preparatory Statistics

Preparatory Statistics. Beta Handbook: all (most) distributions Wikibook in statistics: http:en.wikibooks.org/Statistics MIT open course: EE&CS: Introductory Probability and Statistics Mainly as lookup literature. Kolmogorov Axioms. Fundamentals of Bayes ’ method.

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Preparatory Statistics

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  1. Preparatory Statistics • Beta Handbook: all (most) distributions • Wikibook in statistics: http:en.wikibooks.org/Statistics • MIT open course: EE&CS:Introductory Probability and Statistics • Mainly as lookup literature

  2. Kolmogorov Axioms

  3. Fundamentals of Bayes’ method • D - observations from simple or complex Observation space • - state or parameter from state or parameter space  P- Likelihood function: probability that D is generated in state f - prior: what we know about the statef(|D)- posterior: what we know after seeing D Sign between right and left part: proportionality. Multiply rightpart with c normalising the left part so it integrates to 1 over 

  4. State space alternatives • Discrete, two or more states (e.g., diseased, non-diseased) • Continuous, e.g., an interval of reals • High-dimensional, a vector of reals (e.g., target position and velocity) • Composite, a vector plus a label(e.g., missile type x at point p with velocity v) • Product space: vector of non-fixed dimension(e.g., one or more targets approaching,one or more disease-causing genes) • Voxel set (medical imaging, atmosphere mapping

  5. Testing for disease • State space (d,n), disease or not • Observation space (P,N) positive or negative • Prior: 0.1% of population has disease,prior is (0.001,0.999) • Likelihood: Test gives 5% false negatives, 10% false positives: P: (0.95, 0.1), N: (0.05, 0.90)

  6. Testing for disease • State space (d,n), disease or not • Observation space (P,N) positive or negative • Prior: 0.1% of population has disease,prior is Prior=[0.001,0.999] • Likelihood: 5% false negatives, 10% false positives: P: [0.95, 0.10], N: [0.05, 0.90] • Combing prior and likelihood:Prior.*P=[0.00095,0.0999]; ->[0.01,0.99]Prior.*N=[0.00005,0.8991]; ->[0.0001, 0.9999]

  7. Deciding target type • Attack aircraft: small, dynamic • Bomber aircraft: large, dynamic • Civilian: Large, slow dynamics • Prior: (0.5,0.4,0.1); • Observer 1: probably small, likelihood (0.8,0.1,0.1); • Observer 2: probably fast,likelihood (0.4,0.4,0.2);

  8. Target classification, Matlab >> prior=[0.5,0.4,0.1 ]; >> lik1=[0.8,0.1,0.1 ]; >> lik2=[0.4,0.4,0.2]; >> post1=prior.*lik1; post1=post1/sum(post1) post1 = 0.8889 0.0889 0.0222 >> post2=prior.*lik2; post2=post2/sum(post2) post2 = 0.5263 0.4211 0.0526 >> post12=post1.*lik2; post12=post12/sum(post12) post12 = 0.8989 0.0899 0.0112

  9. Odds and Bayes’ Factor Odds for A against B is P(A|D)/P(B|D): >> OddsCiv=post12(3)/(1-post12(3)) %Civilian vs not OddsCiv = 0.0114 >> OddsAtt=post12(1)/(1-post12(1)) % Attack vs not OddsAtt = 8.8889 >> In first case the Odds is conveniently low, not CSecond case high, probably A

  10. Inference on a probability • Bayes’ original problem: estimating success probability p from experiment with f failures and s successes, n=s+f; • Prior is uniform probability for p; • In particular s=9; n=12; • Likelihood:

  11. Estimating a probability >> p=[0:0.01:1]; >> likh=p.^9.*(1-p).^3; >> posterior=likh/sum(likh); >> plot(p) >> print -depsc beta >> NOTE: In Lecture notes example, s and f are swappedand the computation is analytic instead of numeric!

  12. Estimating a probability >>postcum=cumsum(post); >> plot(p,postcum,'b-',[0,1],[0.025 0.025],…'r-',[0,1],[0.975 0.975],'r-'); >> 95% credible interval 95% credible intervalfor p: [0.46, 0.91]in other words, fairness is notrejected. Estimate p byposterior mean: >> sum(posterior.*p) ans = 0.7143 >> postcum([50:51]) ans = 0.0427 0.0497

  13. Is the coin balanced (LN 2.1.10)? • Use outcome D:(s,f) in flipping n=s+f times • Evaluate using two models, one H_r where probability is 0.5, one H_u where it is uniformlydistributed over [0,1]. • P(D:(s,f)|H_r) = 2^(-n) • P(D:(s,f)|H_u) = s!f!/(n+1)! (normalization in Beta dist) • For s=3, f=9, Bayes factor P(D|H_u)/P(D|H_r)1.4, orP(H_r|D)  0.42 ; P(H_u|D)  0.58 HW 1

  14. Is the coin balanced (LN 2.1.10)? >> s=3;f=9; >> gamma(s+1)*gamma(f+1)/gamma(s+f+2)*2^(s+f) ans = 1.4322 >> s=6; f=18; >> gamma(s+1)*gamma(f+1)/gamma(s+f+2)*2^(s+f) ans = 4.9859 >> s=30;f=90; >> gamma(s+1)*gamma(f+1)/gamma(s+f+2)*2^(s+f) ans = 6.4717e+05 % in logs: >> exp(gammaln(s+1)+gammaln(f+1)-gammaln(s+f+2)+…log(2)*(s+f)) ans = 6.4717e+05

  15. Dissecting Master Bayes’ formula • Parametrized and composite models:

  16. Recursive & Dynamic inference • Repeated measurements improve accuracy: • Chapman Kolmogorov, tracking in time:

  17. Retrodiction: what happened? Retrodiction(smoothing) gives additional precision, but later

  18. MCMC: PET camera likelihood prior D: film, count by detector jX: radioactivity in voxel ia_ij: camera geometry Fraction of emission from voxel i reaching detector j Inference about X gives posterior, its mean is often a good picture of patient

  19. MCMC: PET camera likelihood prior MCMC: Stochastic solution of probability problems Generate sequence of states with the same distributionas the posterior. In this case (X1, X2, …). Each memberis a full 3D image. ESTIMATE X by taking mean over trace.

  20. MCMC: PET camera likelihood prior

  21. MCMC: PET camera Main MCMC loop: We have (X1, X2, … Xk) and wantto compute X(k+1). Propose a new image Z by changing the value in one voxel Compute a=(Z)/(Xk), acceptance probability. Accept X(k+1)=Z if a>1 or with probability a. If not accept, X(k+1)=Xk. Matlab: if a>rand X(k+1)=Z else X(k+1)=X(k) end; In practise: Compute in logarithms to avoid underflowDifferential computation: Most of the terms in (Z) same as in (Xk)

  22. Sinogram and reconstruction Tumour Fruit FlyDrosophila family (Xray)

  23. Does Bayes give the right answer? • Output is a posterior. How accurate?Depends on prior and likelihood assessed. • If data is generated by distribution g(x)and inference is for parameter of f(x|)then asymptotically posterior for  willconcentrate on argmin KL(g(.),f(.| )) KL: Kullback-Leibler distance

  24. Does Bayes give right answer? • Coherence:If you evaluate bets on uncertain events, anyone who does not use Bayes’ rule to evaluate will potentially loose unlimited amount to you who use Bayes’ rule. (Freedman Purves, 1969) • Consistency:Observing properties of Boolean (set) algebra, the calculus of plausibility has to be embeddable in an ordered field where + and  correspond to the combination functions for deriving plausibilities of disjunction and conjunction. (Jaynes Ch 2, Arnborg, Sjödin MaxEnt 2000, ECCAI 2000)

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