Computer Vision Group Prof. Daniel Cremers. 10a. Markov Chain Monte Carlo

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1 Group Prof. Daniel Cremers 10a. Markov Chain Monte Carlo

2 Markov Chain Monte Carlo In high-dimensional spaces, rejection sampling and importance sampling are very inefficient An alternative is Markov Chain Monte Carlo (MCMC) It keeps a record of the current state and the proposal depends on that state Most common algorithms are the Metropolis- Hastings algorithm and Gibbs Sampling 2 Group

3 Markov Chains Revisited A Markov Chain is a distribution over discretestate random variables x 1,...,x M p(x 1,...,x T )=p(x 1 )p(x 2 x 1 ) = p(x 1 ) so that The graphical model of a Markov chain is this: TY t=2 p(x t x t 1 ) T We will denote as a row vector p(x t x t 1 ) t A Markov chain can also be visualized as a state transition diagram. 3 Group

4 The State Transition Diagram time k=1 A 11 A 11 states k=2 k=3 A 33 A 33 t-2 t-1 t 4 Group

5 The Stationary Distribution KX The probability to reach state k is Or, in matrix notation: t = t 1 A k,t = i=1 i,t 1 A ik We say that t is stationary if t = t 1 To find the stationary distribution we need to solve the eigenvector problem A T v = v. The stationary distribution is then = v T where v is the eigenvector for which the eigenvalue is 1. This eigenvector needs to be normalized so that it is a valid distribution. 5 Group

6 Existence of a Stationary Distribution A Markov chain can have many stationary distributions 0.1 Necessary for having a unique stationary 0.1 distribution: we can reach every state from any other state in finite steps (the chain is regular) A transition distribution t satisfies the property of detailed balance if i A ij = j A ji The chain is then said to be reversible 6 Group

7 Reversible Chain: Example 1 3 A 31 + A 13 A A 13 + t-1 t 7 Group

8 Existence of a Stationary Distribution Theorem: If a Markov chain with transition matrix A is regular and satisfies detailed balance wrt. the distribution, then is a stationary distribution of the chain. Proof: KX KX K X i A ij = j A ji = j A ji = j 8j i=1 i=1 i=1 it follows. = A This is only a sufficient condition, however it is not necessary. 8 Group

9 Sampling with a Markov Chain The main idea of MCMC is to sample state transitions based on a proposal distribution q. The most widely used algorithm is the Metropolis-Hastings (MH) algorithm. In MH, the decision whether to stay in a given state is based on a given probability. If the proposal distribution is stay in state x 0 with probability min Unnormalized target distribution q(x 0 x) 1, p(x0 )q(x x 0 ) p(x)q(x 0 x), then we 9 Group

10 The Metropolis-Hastings Algorithm Initialize x 0 for define s =0, 1, 2,... sample compute acceptance probability compute x = x s x 0 q(x 0 x) = p(x0 )q(x x 0 ) p(x)q(x 0 x) r =min(1, ) sample u U(0, 1) set new sample to x s+1 = ( x 0 if u<r x s if u r 10 Group

11 Why Does This Work? We have to prove that the transition probability of the MH algorithm satisfies detailed balance wrt the target distribution. Theorem: If p MH (x 0 x) is the transition probability of the MH algorithm, then p(x)p MH (x 0 x) =p(x 0 )p MH (x x 0 ) Proof: 11 Group

12 Why Does This Work? We have to prove that the transition probability of the MH algorithm satisfies detailed balance wrt the target distribution. Theorem: If p MH (x 0 x) is the transition probability of the MH algorithm, then p(x)p MH (x 0 x) =p(x 0 )p MH (x x 0 ) Note: All formulations are valid for discrete and for continuous variables! 12 Group

13 Choosing the Proposal A proposal distribution is valid if it gives a nonzero probability of moving to the states that have a non-zero probability in the target. A good proposal is the Gaussian, because it has a non-zero probability for all states. However: the variance of the Gaussian is important! with low variance, the sampler does not explore sufficiently, e.g. it is fixed to a particular mode with too high variance, the proposal is rejected too often, the samples are a bad approximation 13 Group

14 Example MH with N(0, ) proposal Target is a mixture of 2 1D Gaussians. Proposal is a Gaussian with different variances Iterations Samples MH with N(0, ) proposal MH with N(0, ) proposal Iterations Samples Iterations Samples Group

15 Gibbs Sampling Initialize {z i : i =1,...,M} For Sample Sample... =1,...,T Sample z ( +1) 1 p(z 1 z ( ) 2,...,z( ) M ) z ( +1) 2 p(z 2 z ( +1) 1,...,z ( ) M ) z ( +1) M p(z M z ( +1) 1,...,z ( +1) M 1 ) Idea: sample from the full conditional This can be obtained, e.g. from the Markov blanket in graphical models. 15 Group

16 Gibbs Sampling: Example Use an MRF on a binary image with edge potentials (x s,x t )=exp(jx s x t ) ( Ising model ) and node potentials (x t )=N (y t x t, 2 ) x t 2 { 1, 1} y t x s x t 16 Group

17 Gibbs Sampling: Example Use an MRF on a binary image with edge potentials (x s,x t )=exp(jx s x t ) ( Ising model ) and node potentials (x t )=N (y t x t, 2 ) Sample each pixel in turn sample 1, Gibbs 1 sample 5, Gibbs 1 mean after 15 sweeps of Gibbs After 1 sample After 5 samples Average after 15 samples 17 Group

18 Gibbs Sampling for GMMs Again, we start with the full joint distribution: p(x, Z, µ,, ) =p(x Z, µ, )p(z )p( ) (semi-conjugate prior) It can be shown that the full conditionals are: KY k=1 p(z i = k x i, µ,, ) / k N (x i µ k, k ) NX p( z) =Dir({ k + z ik } K k=1) i=1 p(µ k k,z,x)=n(µ k m k,v k ) p( k µ k,z,x)=iw( k S k, k ) p(µ k )p( k ) (linear-gaussian) 18 Group

19 Gibbs Sampling for GMMs First, we initialize all variables Then we iterate over sampling from each conditional in turn In the end, we look at and µ k k 10 8 "data_gibbs.dat" 10 8 class 1 class 2 mu1 mu2 sigma1 sigma Group

20 How Often Do We Have To Sample? 8 7 mu1x mu1y mu2x mu2y Here: after 50 sample rounds the values don t change any more In general, the mixing time is related to the eigen gap of the transition matrix: = 1 2 apple O( 1 log n ) 20 Group

21 Gibbs Sampling is a Special Case of MH The proposal distribution in Gibbs sampling is q(x 0 x) =p(x 0 i x i )I(x 0 i = x i ) This leads to an acceptance rate of: = p(x0 )q(x x 0 ) p(x)q(x 0 x) = p(x0 i x0 i )p(x0 i )p(x i x 0 i ) p(x i x i )p(x i )p(x 0 i x i) =1 Although the acceptance is 100%, Gibbs sampling does not converge faster, as it only updates one variable at a time. 21 Group

22 Summary Markov Chain Monte Carlo is a family of sampling algorithms that can sample from arbitrary distributions by moving in state space Most used methods are the Metropolis-Hastings (MH) and the Gibbs sampling method MH uses a proposal distribution and accepts a proposed state randomly Gibbs sampling does not use a proposal distribution, but samples from the full conditionals 22 Group

23 Group Prof. Daniel Cremers 11. Evaluation and Model Selection

24 Evaluation of Learning Methods Very often, machine learning tries to find parameters of a model for a given data set. But: Which parameters give a good model? Intuitively, a good model behaves well on new, unseen data. This motivates the distinction of Training data: used to generate different models Validation data: used to adjust the model parameters so that their performance is optimal Test data: used to evaluate the models with the optimized parameters 24 Group

25 Loss Function A common way to evaluate a learning algorithm (e.g. regression, classification) is to define a loss function: In the case of regression, a common choice is the squared loss In classification, where numbers, we use the 0/1-loss: and t are natural 25 Group

26 Some Loss Functions 2 x x x L(y, a) = y Quadratic loss is useful for continuous parameters a q a = action, e.g. labeling Its minimum is the posterior mean E[y x] Absolute loss is less sensitive to outliers Its minimum is the median of the posterior 26 Group

27 Model Selection Model selection is used to find model parameters to optimize the classification result. Possible methods: Minimizing the training error Hold-out testing Cross-validation Leave-one-out rule Evaluation is done using an appropriate loss function. 27 Group

28 Minimizing the Training Error The training error is defined as: Where are all input/target value pairs from the training data set. Problem: A model that minimizes the training error does not (necessarily) generalize well. It only behaves well on the data it was trained with. Example: Polynomial regression with high model complexity (see above) 28 Group

29 Hold-out Testing Hold-out testing splits the data set up into A validation data set of size A smaller training data set of size The model parameters are then selected so that the error is minimized. Problems: How big should be? Which elements should be chosen for evaluation? Also: Iteration of the model design may lead to overfitting on the validation data 29 Group

30 Hold-out Testing Training set Validation set 30 Group

31 Cross Validation Idea of cross validation: Perform hold-out testing times on different evaluation (sub-)sets. Validation subsets: Error function: Minimizing this error function gives good results, but requires huge computational efforts. The training must be done times. 31 Group

32 Cross-Validation Training set 32 Validation set Group

33 Cross-Validation Training set 33 Group

34 Cross-Validation Training set 34 Group

35 Leave-one-out Rule Idea: do cross-validation with Yields similarly good results compared to crossvalidation Still requires to do the training times Useful if the data set is particularly scarce 35 Group

36 Leave-one-out Rule Training set 36 Validation set Group

37 Leave-one-out Rule Training set 37 Validation set Group

38 BIC and AIC Observation: There is a trade-off between the obtained data likelihood (i.e. the quality of the fit) and the complexity M of the model. Idea: Define a score function that balances both. Two common examples are: Akaike Information Criterion Bayesian Information Criterion But: These criteria tend to favor too simple models. 38 Group

39 Classifier Evaluation Different methods to evaluate a classifier True-positive / false-positive rates Precision-recall diagram Receiving operator characteristics (ROC curves) Loss function The evaluation of a classifier is needed to find the best classification parameters (e.g. for the kernel function) 39 Group

40 Classifier Evaluation Number of all data points: Classification result = -1 Classification result = 1 Correct label = -1 True negative rate False positive rate Correct label = 1 False negative rate Number of positive examples Number of negative examples 40 True positive rate Group

41 Classifier Evaluation Terminology We define the precision as intuitively: probability that the real label is positive in case the classifier returns positive The recall is defined as intuitively: probability that a positive labeled data point is detected as positive by the classifier. 41 Group

42 Precision-Recall Curves Usually, precision and recall are plotted into the same graph. The optimal classifier parameters are obtained where precision equals recall (break-even-point). 42 Group

43 Receiver Operating Characteristics Receiving Operating Characteristics (ROC) curves plot the true positive rate vs. the false positive rate. To find a good classifier we can search for a point where the slope is Group

44 Loss Function Another method to evaluate a classifier is defined by evaluating its loss function. The simplest loss function is the 0/1-loss: Where is a classifier, is a feature vector and is the known class label of. Important: The pair should be taken from an evaluation data set that is different from the training set. 44 Group