# Week 3 - PowerPoint PPT Presentation Download Presentation Week 3

Week 3
Download Presentation ## Week 3

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
##### Presentation Transcript

1. Week 3 Pattern Recognition and Machine Learning Chapter 2: Probability distributions

2. Bayesian Inference for the Gaussian (1) Assume ¾2 is known. Given i.i.d. data , the likelihood function for¹ is given by This has a Gaussian shape as a function of ¹ (but it is not a distribution over ¹).

3. Bayesian Inference for the Gaussian (2) Combined with a Gaussian prior over ¹, this gives the posterior Let, the posterior be

4. Bayesian Inference for the Gaussian (3) Then, Note:

5. Bayesian Inference for Gaussian (4) Example: for N = 0, 1, 2 and 10.

6. Sequential Estimation of Parameters usingBayesian Inference for the Gaussian The posterior obtained after observing N { 1 data points becomes the prior when we observe the Nth data point.

7. Sequential Estimation using MLE of Gaussian Contribution of the Nth data point, xN correction given xN correction weight old estimate

8. Sequential Estimation using The Robbins-Monro Algorithm Consider the log likelihood function averaged over N as N goes to infinity =0 Goal: Solve this equation sequentially for µN given µN -1

9. The Robbins-Monro Algorithm (1) Consider µ and z governed by p(z,µ) and define the regression function • Seek µ? such that f(µ?) = 0.

10. The Robbins-Monro Algorithm (2) Assume we are given samples from p(z,µ), one at the time.

11. The Robbins-Monro Algorithm (3) • Successive estimates of µ? are then given by • Conditions on aN for convergence :

12. Robbins-Monro for Maximum Likelihood (4) Compare to as a regression function, finding its root is equivalent to finding the maximum likelihood solution µML. Thus

13. Robbins-Monro for Maximum Likelihood (5) Example: estimate the mean of a Gaussian. The distribution of z is Gaussian with mean ¹ { ¹ML. For the Robbins-Monro update equation, aN = ¾2=N.

14. Bayesian Inference for the Gaussian (6) Now assume ¹ is known. The likelihood function for ¸ = 1/¾2 is given by This has a Gamma shape as a function of ¸.

15. Bayesian Inference for the Gaussian (7) The Gamma distribution

16. Bayesian Inference for the Gaussian (8) Now we combine a Gamma prior, ,with the likelihood function for ¸ to obtain for posterior with

17. Bayesian Inference for the Gaussian (9) If both ¹ and ¸ are unknown, the joint likelihood function is given by We need a conjugate prior with the same functional dependence on ¹ and ¸.

18. Bayesian Inference for the Gaussian (10) The Gaussian-gamma distribution • Quadratic in ¹. • Linear in ¸. • Gamma distribution over ¸. • Independent of ¹.

19. Bayesian Inference for the Gaussian (11) The Gaussian-gamma distribution

20. Bayesian Inference for the Gaussian (12) Multivariate conjugate priors ¹ unknown, ¤ known: p(¹) Gaussian. ¤ unknown, ¹ known: p(¤) Wishart, ¤ and ¹ unknown: p(¹,¤) Gaussian-Wishart,

21. A Robust Distribution as an Alternative to Gaussian: Student’s t-Distributionby Gosset where :Degree of freedom Infinite mixture of Gaussians.

22. Student’s t-Distribution

23. Student’s t-Distribution Robustness to outliers: Gaussianvst-distribution.

24. Student’s t-Distribution The D-variate case: where . Properties:

25. Another Interesting Distribution forPeriodic rv’s- Von Mises Distribution Example: Temperature of a season in a day, … We require

26. θn: periodic variable as two- dimensional vectors xn living around the unit circle.

27. von Mises Distribution (1) This requirement is satisfied by where is the 0th order modified Bessel function of the 1st kind.

28. von Mises Distribution (4)

29. Maximum Likelihood for von Mises Given a data set, , the log likelihood function is given by Maximizing with respect to µ0 we directly obtain Similarly, maximizing with respect to m we get which can be solved numerically for mML.

30. Mixtures of Gaussians (1) Old Faithful data set Single Gaussian Mixture of two Gaussians

31. Mixtures of Gaussians (2) Combine simple models into a complex model: Component Mixing coefficient K=3

32. Mixtures of Gaussians (3)

33. Mixtures of Gaussians (4) Determining parameters ¹, §, and ¼ using maximum log likelihood Solution: use standard, iterative, numeric optimization methods or the expectation maximization algorithm (Chapter 9). Log of a sum; no closed form maximum.

34. EXPONENTİALS : Gaussian Bernoulli Binomial Beta Gamma Multinomial Dirichlet Gaussian Gama Student Von Mises

35. The Exponential Family (1) where ´ is the natural parameter and so g(´) can be interpreted as a normalization coefficient.

36. Bernoulli: and so Logistic sigmoid

37. The Multinomial: where, , and NOTE: The ´k parameters are not independent since the corresponding ¹k must satisfy

38. Let . This leads to and Here the ´k parameters are independent. Note that and Softmax

39. The Multinomial distribution can then be written as

40. MLEfor the Exponential Family

41. ML for the Exponential Family (2) Give a data set, , the likelihood function is given by Take log, then take derivative Sufficient statistic

42. Conjugate prior of likelihood İs Combining with the likelihood function, we get Prior corresponds to º pseudo-observations with value Â.

43. Noninformative Priors (1) With little or no information available a-priori, we might choose a non-informative prior. ¸ discrete, K-nomial : ¸2[a,b] real and bounded: ¸ real and unbounded: improper! A constant prior may no longer be constant after a change of variable; consider p(¸) constant and ¸=´2:

44. Noninformative Priors (2) Translation invariant priors. Consider For a corresponding prior over ¹, we have for any A and B. Thus p(¹) = p(¹ { c) and p(¹) must be constant.

45. Noninformative Priors (3) Example: The mean of a Gaussian, ¹ ; the conjugate prior is also a Gaussian, As , this will become constant over ¹ .

46. Noninformative Priors (4) Scale invariant priors. Consider and make the change of variable For a corresponding prior over ¾, we have for any A and B. Thus p(¾) 1/¾ and so this prior is improper too. Note that this corresponds to p(ln¾) being constant.

47. Noninformative Priors (5) Example: For the variance of a Gaussian, ¾2, we have If ¸= 1/¾2 and p(¾) / 1/¾ , then p(¸) / 1/ ¸. We know that the conjugate distribution for ¸ is the Gamma distribution, A noninformative prior is obtained when a0 = 0 and b0 = 0.

48. Nonparametric Methods (1) Parametric distribution models are restricted to specific forms, which may not always be suitable; for example, consider modelling a multimodal distribution with a single, unimodal model. Nonparametric approaches make few assumptions about the overall shape of the distribution being modelled.
{"ft_lang_label":"__label__en","ft_lang_prob":0.80151033,"math_prob":0.9774174,"size":6167,"snap":"2020-10-2020-16","text_gpt3_token_len":1520,"char_repetition_ratio":0.16615285,"word_repetition_ratio":0.035282258,"special_character_ratio":0.2271769,"punctuation_ratio":0.116651826,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.99691314,"pos_list":[0,1,2,3,4,5,6,7,8],"im_url_duplicate_count":[null,4,null,null,null,2,null,null,null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2020-04-07T17:46:40Z\",\"WARC-Record-ID\":\"<urn:uuid:286d500f-669c-4c38-aa63-dbe84e68e9b6>\",\"Content-Length\":\"56912\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:dd13eaba-a3d9-4b84-a094-7d8edb032e63>\",\"WARC-Concurrent-To\":\"<urn:uuid:14242150-20ef-4570-855d-6d0ae2da0e71>\",\"WARC-IP-Address\":\"52.10.219.111\",\"WARC-Target-URI\":\"https://www.slideserve.com/paco/week-3-powerpoint-ppt-presentation\",\"WARC-Payload-Digest\":\"sha1:N7HK67YVKISNN7PDI5AF3CRF4KM54AYB\",\"WARC-Block-Digest\":\"sha1:KJMNWXKS6RWVLIIS2KDEFE5L7BIGKPFX\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2020/CC-MAIN-2020-16/CC-MAIN-2020-16_segments_1585371803248.90_warc_CC-MAIN-20200407152449-20200407182949-00541.warc.gz\"}"}