View source for Segment 28. Gaussian Mixture Models in 1-D

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The direct YouTube link is [http://youtu.be/n7u_tq0I6jM http://youtu.be/n7u_tq0I6jM]

Links to the slides: [http://wpressutexas.net/coursefiles/28.GaussianMixtureModels1D.pdf PDF file] or [http://wpressutexas.net/coursefiles/28.GaussianMixtureModels1D.ppt PowerPoint file]

===Problems===
====To Calculate====
1. Draw a sample of 100 points from the uniform distribution <math>U(0,1)</math>.  This is your data set.  Fit GMM models to your sample (now considered as being on the interval <math>-\infty < x < \infty</math>) with increasing numbers of components <math>K</math>, at least <math>K=1,\ldots,5</math>.  Plot your models.  Do they get better as <math>K</math> increases?  Did you try multiple starting values to find the best (hopefully globally best) solutions for each <math>K</math>?

2. Multiplying a lot of individual likelihoods will often underflow.  (a) On average, how many values drawn from <math>U(0,1)</math> can you multiply before the product underflows to zero? (b) What, analytically, is the distribution of the sum of <math>N</math> independent values <math>\log(U)</math>, where <math>U\sim U(0,1)</math>?  (c) Is your answer to (a) consistent with your answer to (b)?

====To Think About====
1.  Suppose you want to approximate some analytically known function <math>f(x)</math> (whose integral is finite), as a sum of <math>K</math> Gaussians with different centers and widths.  You could pretend that <math>f(x)</math> (or some scaling of it) was a probability distribution, draw <math>N</math> points from it and do the GMM thing to find the approximating Gaussians.  Now take the limit <math>N\rightarrow \infty</math>, figure out how sums become integrals, and write down an iterative method for fitting Gaussians to a given <math>f(x)</math>.  Does it work?  (You can assume that well-defined definite integrals can be done numerically.)

===Class Activity===

Let's explore a data set and try to make sensible statements about it.

[[Media:netflixishdata.txt|netflixishdata.txt]]

Rows are 200 movie watchers, columns are 100 movies, entries are their ratings on a scale
of 1 (I hated it!) to 5 (I loved it!).  This is not real data, of course, so it is only Netflixish, not Netflix.

Questions to explore

How much are people alike? <br/>
How much are movies alike?  <br/>
Distribution of the data in various ways?  <br/>

By summing over all the columns and dividing by number of entires, we got the average rating for each movie. Something surprising was that the max of all the mean ratings was 3.46 (so there was no mean rating greater than 3.46 stars!), the min was 2.3650, the mean of all the mean movie ratings is 2.9998, the median of these mean ratings was 2.9925. <br/>

Insight #1:<br/>
Looking at the actual data set, we see that there are a lot of "haters" i.e. there are a lot of people who gave a lot of 1 ratings.

[[File:28ca1.png|thumb|center|700px|link=|alt=]]

Insight #2:<br/>
Overall there are exactly 4000 (+ or - 1) of each rating.

[[File:28ca2.png|thumb|center|700px|link=|alt=]]

Insight #3 There seem to be exactly 4 kinds of movies:<br/>

Why? Movie ratings were generated from the sides of a regular tetrahedron

[[File:28ca3.png|thumb|center|700px|link=|alt=]]