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# Bayesian Essentials and Bayesian Regression

Download Presentation ## Bayesian Essentials and Bayesian Regression

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1. Bayesian Essentials and Bayesian Regression

2. Y 1 1 X Distribution Theory 101 • Marginal and Conditional Distributions: uniform

3. Simulating from Joint • To draw from the joint: • i. draw from marginal on X • ii. Condition on this draw, and draw from conditional of Y|X

4. The Goal of Inference • Make inferences about unknown quantities using available information. • Inference -- make probability statements • unknowns -- • parameters, functions of parameters, states or latent variables, “future” outcomes, outcomes conditional on an action • Information – • data-based • non data-based • theories of behavior; “subjective views” there is an underlying structure • parameters are finite or in some range

5. Data Aspects of Marketing Problems • Ex: Conjoint Survey • 500 respondents rank, rate, chose among product configurations. • Small Amount of Information per respondent • Response Variable Discrete • Ex: Retail Scanning Data • very large number of products • large number of geographical units (markets, zones, stores) • limited variation in some marketing mix vars • Must make plausible predictions for decision making!

6. The likelihood principle Note: any function proportional to data density can be called the likelihood. • LP: the likelihood contains all information relevant for inference. That is, as long as I have same likelihood function, I should make the same inferences about the unknowns. • Implies analysis is done conditional on the data,.

7. Bayes theorem • p(|D)  p(D| ) p() • Posterior  “Likelihood” × Prior • Modern Bayesian computing– simulation methods for generating draws from the posterior distribution p(|D).

8. Summarizing the posterior Output from Bayesian Inf: A high dimensional dist Summarize this object via simulation: marginal distributions of don’t just compute

9. Prediction See D,compute: “Predictive Distribution”

10. Decision theory • Loss: L(a,) where a=action; =state of nature • Bayesian decision theory: • Estimation problem is a special case:

11. Sampling properties of Bayes estimators An estimator is admissible if there exist no other estimator with lower risk for all values of . The Bayes estimator minimizes expected (average) risk which implies they are admissible: The Bayes estimator does the best for every D. Therefore, it must work as at least as well as any other estimator.

12. Bayes Inference: Summary • Bayesian Inference delivers an integrated approach to: • Inference – including “estimation” and “testing” • Prediction – with a full accounting for uncertainty • Decision – with likelihood and loss (these are distinct!) • Bayesian Inference is conditional on available info. • The right answer to the right question. • Bayes estimators are admissible. All admissible estimators are Bayes (Complete Class Thm). Which Bayes estimator?

13. Bayes/Classical Estimators Prior washes out – locally uniform!!! Bayes is consistent unless you have dogmatic prior.

14. Benefits/Costs of Bayes Inf Benefits- finite sample answer to right question full accounting for uncertainty integrated approach to inf/decision “Costs”- computational (true any more? < classical!!) prior (cost or benefit?) esp. with many parms (hierarchical/non-parameric problems)

15. Bayesian Computations Before simulation methods, Bayesians used posterior expectations of various functions as summary of posterior. If p(θ|D) is in a convenient form (e.g. normal), then I might be able to compute this for some h. Via iid simulation for all h.

16. Conjugate Families • Models with convenient analytic properties almost invariably come from conjugate families. • Why do I care now? • - conjugate models are used as building blocks • build intuition re functions of Bayesian inference • Definition: • A prior is conjugate to a likelihood if the posterior is in the same class of distributions as prior. • Basically, conjugate priors are like the posterior from some imaginary dataset with a diffuse prior.

17. Need a prior! Beta-Binomial model

18. Beta distribution

19. Posterior

20. Prediction

21. Regression model Is this model complete? For non-experimental data, don’t we need a model for the joint distribution of y and x?

22. two separate analyses Regression model simultaneous systems are not written this way! rules out x=f(β)!!! If Ψ is a priori indep of (β,σ),

23. Conjugate Prior What is conjugate prior? Comes from form of likelihood function. Here we condition on X. quadratic form suggests normal prior. Let’s – complete the square on βor rewrite by projecting y on X (column space of X).

24. y x2 2x2 1x1 x1 Geometry of regression

25. Traditional regression • No one ever computes a matrix inverse directly. • Two numerically stable methods: • QR decomposition of X • Cholesky root of X’X and compute inverse using root • Non-Bayesians have to worry about singularity or near singularity of X’X. We don’t! more later

26. Cholesky Roots In Bayesian computations, the fundamental matrix operation is the Cholesky root. chol() in R The Cholesky root is the generalization of the square root applied to positive definite matrices. As Bayesians with proper priors, we don’t ever have to worry about singular matrices! U is upper triangular with positive diagonal elements. U-1 is easy to compute by recursively solving TU = I for T, backsolve() in R.

27. Cholesky Roots Cholesky roots can be useful to simulate from Multivariate Normal Distribution. To simulate a matrix of draws from MVN (each row is a separate draw) in R, Y=matrix(rnorm(n*k),ncol=k)%*%chol(Sigma) Y=t(t(Y)+mu)

28. data.txt: UNIT Y X1 X2 A 1 0.23815 0.43730 A 2 0.55508 0.47938 A 3 3.03399 -2.17571 A 4 -1.49488 1.66929 B 10 -1.74019 0.35368 B 9 1.40533 -1.26120 B 8 0.15628 -0.27751 B 7 -0.93869 -0.04410 B 6 -3.06566 0.14486 df=read.table("data.txt",header=TRUE) myreg=function(y,X){ # # purpose: compute lsq regression # # arguments: # y -- vector of dep var # X -- array of indep vars # # output: # list containing lsq coef and std errors # XpXinv=chol2inv(chol(crossprod(X))) bhat=XpXinv%*%crossprod(X,y) res=as.vector(y-X%*%bhat) ssq=as.numeric(res%*%res/(nrow(X)-ncol(X))) se=sqrt(diag(ssq*XpXinv)) list(b=bhat,std_errors=se) } Regression with R

29. where Regression likelihood

30. Regression likelihood This is called an inverted gamma distribution. It can also be related to the inverse of a Chi-squared distribution. Note the conjugate prior suggested by the form the likelihood has a prior on βwhich depends on σ.

31. Bayesian Regression Prior: Interpretation as from another dataset. Inverted Chi-Square: Draw from prior?

32. Posterior

34. Posterior

35. IID Simulations Scheme: [y|X, , 2] [|2] [2] 1) Draw [2 | y, X] 2) Draw [ | 2,y, X] 3) Repeat

36. IID Simulator, cont.

37. Bayes Estimator The Bayes Estimator is the posterior mean of β. Marginal on β is a multivariate student t. Who cares?

38. Shrinkage and Conjugate Priors The Bayes Estimator is the posterior mean of β. This is a “shrinkage” estimator. Is this reasonable?

39. Assessing Prior Hyperparameters These determine prior location and spread for both coefs and error variance. It has become customary to assess a “diffuse” prior: This can be problematic. Var(y) might be a better choice.

40. Improper or “non-informative” priors Classic “non-informative” prior (improper): • Is this “non-informative”? • Of course not, it says that  is large with high prior “probability” • Is this wise computationally? • No, I have to worry about singularity in X’X • Is this a good procedure? • No, it is not admissible. Shrinkage is good!

41. runireg • runireg= • function(Data,Prior,Mcmc){ • # • # purpose: • # draw from posterior for a univariate regression model with natural conjugate prior • # • # Arguments: • # Data -- list of data • # y,X • # Prior -- list of prior hyperparameters • # betabar,A prior mean, prior precision • # nu, ssq prior on sigmasq • # Mcmc -- list of MCMC parms • # R number of draws • # keep -- thinning parameter • # • # output: • # list of beta, sigmasq draws • # beta is k x 1 vector of coefficients • # model: • # Y=Xbeta+e var(e_i) = sigmasq • # priors: beta| sigmasq ~ N(betabar,sigmasq*A^-1) • # sigmasq ~ (nu*ssq)/chisq_nu

42. runireg • RA=chol(A) • W=rbind(X,RA) • z=c(y,as.vector(RA%*%betabar)) • IR=backsolve(chol(crossprod(W)),diag(k)) • # W'W=R'R ; (W'W)^-1 = IR IR' -- this is UL decomp • btilde=crossprod(t(IR))%*%crossprod(W,z) • res=z-W%*%btilde • s=t(res)%*%res • # • # first draw Sigma • # • sigmasq=(nu*ssq + s)/rchisq(1,nu+n) • # • # now draw beta given Sigma • # • beta = btilde + as.vector(sqrt(sigmasq))*IR%*%rnorm(k) • list(beta=beta,sigmasq=sigmasq) • }

43. Multivariate Regression

44. Multivariate regression likelihood

45. Multivariate regression likelihood

46. Inverted Wishart distribution Form of the likelihood suggests that natural conjugate (convenient prior) for  would be of the Inverted Wishart form: denoted • - tightness V- location however, as  increases, spread also increases limitations: i. small  -- thick tail ii. only one tightness parm

47. Wishart distribution (rwishart)

48. Multivariate regression prior and posterior Prior: Posterior: