Introduction to Bayesian Statistics
•Download as ODP, PDF•
6 likes•4,390 views
This document provides an introduction to Bayesian statistics and machine learning. It discusses key concepts like conditional probability, Bayes' theorem, Bayesian inference, Bayesian model comparison, and Bayesian learning. Conditional probability is fundamental in probability theory and looks at the probability of event A given event B. Bayes' theorem allows updating beliefs with new evidence and can be visualized with diagrams. Bayesian inference involves specifying prior distributions over parameters and updating them based on observed data to obtain posterior distributions. Bayesian models can be compared using Bayes factors, which are ratios of marginal likelihoods. Bayesian learning techniques include Markov chain Monte Carlo methods and hierarchical Bayesian models.
More Related Content
What's hot
What's hot (20)
Similar to Introduction to Bayesian Statistics
Recently uploaded
Recently uploaded (20)
Introduction to Bayesian Statistics
- 1. Introduction to Bayesian Statistics Machine Learning and Data Mining Philipp Singer CC image courtesy of user mattbuck007 on Flickr
- 3. 3 Conditional Probability ● Probability of event A given that B is true ● P(cough|cold) > P(cough) ● Fundamental in probability theory
- 4. 4 Before we start with Bayes ... ● Another perspective on conditional probability ● Conditional probability via growing trimmed trees ● https://www.youtube.com/watch?v=Zxm4Xxvzohk
- 6. 6 Bayes Theorem ● P(A|B) is conditional probability of observing A given B is true ● P(B|A) is conditional probability of observing B given A is true ● P(A) and P(B) are probabilities of A and B without conditioning on each other
- 7. 7 Visualize Bayes Theorem Source: https://oscarbonilla.com/2009/05/visualizing-bayes-theorem/ All possible outcomes Some event
- 8. 8 Visualize Bayes Theorem All people in study People having cancer
- 9. 9 Visualize Bayes Theorem All people in study People where screening test is positive
- 10. 10 Visualize Bayes Theorem People having positive screening test and cancer
- 11. 11 Visualize Bayes Theorem ● Given the test is positive, what is the probability that said person has cancer?
- 12. 12 Visualize Bayes Theorem ● Given the test is positive, what is the probability that said person has cancer?
- 13. 13 Visualize Bayes Theorem ● Given that someone has cancer, what is the probability that said person had a positive test?
- 14. 14 Example: Fake coin ● Two coins – One fair – One unfair ● What is the probability of having the fair coin after flipping Heads? CC image courtesy of user pagedooley on Flickr
- 15. 15 Example: Fake coin CC image courtesy of user pagedooley on Flickr
- 16. 16 Example: Fake coin CC image courtesy of user pagedooley on Flickr
- 17. 17 Update of beliefs ● Allows new evidence to update beliefs ● Prior can also be posterior of previous update
- 18. 18 Example: Fake coin CC image courtesy of user pagedooley on Flickr ● Belief update ● What is probability of seeing a fair coin after we have already seen one Heads
- 21. 21 Bayesian Inference ● Statistical inference of parameters Parameters Data Additional knowledge
- 22. 22 Coin flip example ● Flip a coin several times ● Is it fair? ● Let's use Bayesian inference
- 23. 23 Binomial model ● Probability p of flipping heads ● Flipping tails: 1-p ● Binomial model
- 24. 24 Prior ● Prior belief about parameter(s) ● Conjugate prior – Posterior of same distribution as prior – Beta distribution conjugate to binomial ● Beta prior
- 25. 25 Beta distribution ● Continuous probability distribution ● Interval [0,1] ● Two shape parameters: α and β – If >= 1, interpret as pseudo counts – α would refer to flipping heads
- 31. 31 Posterior ● Posterior also Beta distribution ● For exact deviation: http://www.cs.cmu.edu/~10701/lecture/technote2_betabinomial.pdf
- 32. 32 Posterior ● Assume – Binomial p = 0.4 – Uniform Beta prior: α=1 and β=1 – 200 random variates from binomial distribution (Heads=80) – Update posterior
- 33. 33 Posterior ● Assume – Binomial p = 0.4 – Biased Beta prior: α=50 and β=10 – 200 random variates from binomial distribution (Heads=80) – Update posterior
- 34. 34 Posterior ● Convex combination of prior and data ● The stronger our prior belief, the more data we need to overrule the prior ● The less prior belief we have, the quicker the data overrules the prior
- 35. 36 So is the coin fair? ● Examine posterior – 95% posterior density interval – ROPE [1]: Region of practical equivalence for null hypothesis – Fair coin: [0.45,0.55] ● 95% HDI: (0.33, 0.47) ● Cannot reject null ● More samples→ we can [1] Kruschke, John. Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan. Academic Press, 2014.
- 36. 37 Bayesian Model Comparison ● Parameters marginalized out ● Average of likelihood weighted by prior Evidence
- 37. 38 Bayesian Model Comparison ● Bayes factors [1] ● Ratio of marginal likelihoods ● Interpretation table by Kass & Raftery [1] ● >100 → decisive evidence against M2 [1] Kass, Robert E., and Adrian E. Raftery. "Bayes factors." Journal of the american statistical association 90.430 (1995): 773-795.
- 38. 39 So is the coin fair? ● Null hypothesis ● Alternative hypothesis – Anything is possible – Beta(1,1) ● Bayes factor
- 39. 40 So is the coin fair? ● n = 200 ● k = 80 ● Bayes factor ● (Decent) preference for alt. hypothesis
- 40. 41 Other priors ● Prior can encode (theories) hypotheses ● Biased hypothesis: Beta(101,11) ● Haldane prior: Beta(0.001, 0.001) – u-shaped – high probability on p=1 or (1-p)=1
- 41. 42 Frequentist approach ● So is the coin fair? ● Binomial test with null p=0.5 – one-tailed – 0.0028 ● Chi² test
- 42. 43 Posterior prediction ● Posterior mean ● If data large→converges to MLE ● MAP: Maximum a posteriori – Bayesian estimator – uses mode
- 43. 44 Bayesian prediction ● Posterior predictive distribution ● Distribution of unobserved observations conditioned on observed data (train, test) Frequentist MLE
- 44. 45 Alternative Bayesian Inference ● Often marginal likelihood not easy to evaluate – No analytical solution – Numerical integration expensive ● Alternatives – Monte Carlo integration ● Markov Chain Monte Carlo (MCMC) ● Gibbs sampling ● Metropolis-Hastings algorithm – Laplace approximation – Variational Bayes
- 46. 47 Bayesian Models ● Example: Markov Chain Model – Dirichlet prior, Categorical Likelihood ● Bayesian networks ● Topic models (LDA) ● Hierarchical Bayesian models
- 47. 48 Generalized Linear Model ● Multiple linear regression ● Logistic regression ● Bayesian ANOVA
- 48. 49 Bayesian Statistical Tests ● Alternatives to frequentist approaches ● Bayesian correlation ● Bayesian t-test
- 49. 50 Questions? Philipp Singer philipp.singer@gesis.org Image credit: talk of Mike West: http://www2.stat.duke.edu/~mw/ABS04/Lecture_Slides/4.Stats_Regression.pdf