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Lecture 7b: Sampling

Lecture 7b: Sampling. Machine Learning CUNY Graduate Center. Today. Sampling Technique to approximate the expected value of a distribution Gibbs Sampling Sampling of latent variables in a Graphical Model. Expected Values. Want to know the expected value of a distribution.

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Lecture 7b: Sampling

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  1. Lecture 7b: Sampling Machine Learning CUNY Graduate Center

  2. Today • Sampling • Technique to approximate the expected value of a distribution • Gibbs Sampling • Sampling of latent variables in a Graphical Model

  3. Expected Values • Want to know the expected value of a distribution. • E[p(t | x)] is a classification problem • We can calculate p(x), but integration is difficult. • Given a graphical model describing the relationship between variables, we’d like to generate E[p(x)] where x is only partially observed.

  4. Sampling • We have a representation of p(x) and f(x), but integration is intractable • E[f] is difficult as an integral, but easy as a sum. • Randomly select points from distribution p(x) and use these as representative of the distribution of f(x). • It turns out that if correctly sampled, only 10-20 points can be sufficient to estimate the mean and variance of a distribution. • Samples must be independently drawn • Expectation may be dominated by regions of high probability, or high function values

  5. Monte Carlo Example • Sampling techniques to solve difficult integration problems. • What is the area of a circle with radius 1? • What if you don’t know trigonometry?

  6. Monte Carlo Estimation • How can we approximate the area of a circle if we have no trigonometry? • Take a random x and a random y between 1 and -1 • Sample from x and sample from y. • Determine if • Repeat many times. • Count the number of times that the inequality is true. • Divide by the area of the square

  7. Rejection Sampling • The distribution p(x) is easy to evaluate • As in a graphical model representation • But difficult to integrate. • Identify a simpler distribution, kq(x), which bounds p(x), and sample, x0, from it. • This is called the proposal distribution. • Generate another sample u from an even distribution between 0 and kq(x0). • If u ≤ p(x0) accept the sample • E.g. use it in the calculation of an expectation of f • Otherwise reject the sample • E.g. omit from the calculation of an expectation of f

  8. Rejection Sampling Example

  9. Importance Sampling • One problem with rejection sampling is that you lose information when throwing out samples. • If we are only looking for the expected value of f(x), we can incorporate unlikely samples of x in the calculation. • Again use a proposal distributionto approximate the expected value. • Weight each sample from q by the likelihood that it was also drawn from p.

  10. Graphical Example of Importance Sampling

  11. Markov Chain Monte Carlo • Markov Chain: • p(x1|x2,x3,x4,x5,…) = p(x1|x2) • For MCMC sampling start in a state z(0). • At each step, draw a sample z(m+1) based on the previous state z(m) • Accept this step with some probability based on a proposal distribution. • If the step is accepted: z(m+1) = z(m) • Else: z(m+1) = z(m) • Or only accept if the sample is consistent with an observed value

  12. Markov Chain Monte Carlo • Goal: p(z(m)) = p*(z) as m →∞ • MCMCs that have this property are ergodic. • Implies that the sampled distribution converges to the true distribution • Need to define a transition function to move from one state to the next. • How do we draw a sample at state m+1 given state m? • Often, z(m+1) is drawn from a gaussian with z(m) mean and a constant variance.

  13. Markov Chain Monte Carlo • Goal: p(z(m)) = p*(z) as m →∞ • MCMCs that have this property are ergodic. • Transition properties that provide detailed balance guarantee ergodic MCMC processess. • Also considered reversible.

  14. Metropolis-Hastings Algorithm • Assume the current state is z(m). • Draw a sample z* from q(z|z(m)) • Accept probability function • Often use a normal distribution for q • Tradeoff between convergence and acceptance rate based on variance.

  15. Gibbs Sampling We’ve been treating z as a vector to be sampled as a whole However, in high dimensions, the accept probability becomes vanishingly small. Gibbs sampling allows us to sample one variable at a time, based on the other variables in z.

  16. Gibbs sampling Assume a distribution over 3 variables. Generate a new sample for each variable conditioned on all of the other variables.

  17. Gibbs Sampling in a Graphical Model • The appeal of Gibbs sampling in a graphical model is that the conditional distribution of a variable is only dependent on its parents. • Gibbs sampling fixes n-1 variables, and generates a sample for the the nth. • If each of the variables are assumed to have easily sample-able distributions, we can just sample from the conditionals given by the graphical model given some initial states.

  18. Next Time • Perceptrons • Neural Networks

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