Before getting to Cooler Mixture Models
Before going into the depths that is ways to improve the simple Multinomial Mixture Model that I discussed in the last post, I want to give a little adventure story regarding the Gamma distribution. Why the Gamma distribution? Mainly because it is deeply tied to Gaussian distributions and those will factor heavily into cool things you can do in mixture models.
This thing called my Conjugate Prior
So how is the Gamma distribution related to a Gaussian distribution? The short story is that a properly parameterized Gamma distribution can approximate the precision (or inverse variance) for a Gaussian distribution (the proper wording for this relationship is that a Gamma distribution is a conjugate prior for a Gaussian distribution when you know the mean but don’t know the precision). This turns out to be super powerful when you have some idea of what the mean of your Gaussian is but need to estimate the variance. However, this distributions has some flavors that can really wreck havoc on your ability to learn these values if you use them in the wrong place, and similarly people are really terrible at pointing out which flavour you can and should use. So here’s an adventure on taste testing the Gamma distribution.
Sampling from Gamma Distributions
Let’s start with an example. Let’s say we have two one dimensional Gaussian distributions. Each distribution is defined by two parameters: for the mean, or center point, and for the variance, or inverse precision. So let’s say our two distributions have these two parametrizations: and . Here’s a nice density plot of these two distributions together:
Now let’s look at what the Gamma distribution can do if we sample from it with the “proper” parameters (which I’ll point out later on).
It looks like a good approximation of what we know the two variance values to be and the samples can be generated really easily with Scala’s breeze library which you’ll see in the next section. But let’s for funsies sake say that we don’t like using breeze (say because we hate Scala and prefer to trust libraries written in Java), we may then want to use some other implementation of this distribution, like say in Apache Commons Math or Java’s Colt library. What happens if we take the parameters we passed into breeze and passed them to these libraries? What comes out? Let’s See!
That looks kinda funky. What’s going on? This exposes the difference between the two main flavors of this distribution: two related but poorly explained ways to parameterize the model! Both flavours depend on a shape parameter that (aptly named) guides the shape of the distribution. The difference in the flavours is the use of either a scale parameter, sometimes referred to as , or a rate parameter, sometimes denoted as . Is there a relation between these two? Totally! They are inverses of each other, i.e. .
Finding the funky culprit
Now how do we know which of the above three implementations is doing the right thing? One way is to use the Inverse Gamma distribution. Samples from an Inverse Gamma distribution should (roughly) be the reciprocal of samples from a Gamma distribution with the same parameters. But these packages don’t really have an implementation of an Inverse Gamma distribution, so what else can we do to check? Well, Colt looks to be doing two really wierd things that violate our intuitions about variances: (a) one set of variance estimates are completely missing and (b) the one that does exist gives negative variance values, and we know this to be impossible since the variance is defined to be an average of real values raised to the power of two. And if we read the Colt javadoc more carefully, we can figure out that they use the rate and not the scale, but it’s never totally obvious in their documentation. So if we fix that in our parameterization, we get this agreeable set of plots:
That’s much better looking.
Finding the magic parameters
Now let’s dive deeper and see how I made all these (now fixed) samples:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 

In this snippet, I’ve left out data
, but it’s simply a map for accessing the list of
samples I drew earlier to make the Gaussian plot. The key equations here are
for computing what I’m calling alpha
and beta_*
. Looking at a handy table
of conjugate priors for continuous likelihood distributions, we find these
two core equations used in the code:
Where is the number of points in the group and denotes points within coming from the same distribution. Looking at that table of conjugate priors and the list created in the first for loop, you might wonder why i use the reciprocal of the values, i.e. the scales. This is because of the cute footnote in the conjugate prior table noting that
as computed above is in fact the rate.
An even more terrifying parametrization
Now if you thought what I’ve touched on is cool, a little funky, and possible frustrating in the minor differences, it’s time to really complicate things. A really smart man by the name of Carl von Rasmussen designed an Infinite Gaussian Mixture Model that depends heavily on the Gamma distribution for just the purpose I’ve described. BUT he gives this parameterization:
Throw this into our sampling code and we get this rediculous plot:
Which is totally wrong. So what gives? Well, at a much later date, Mr. Rasmussen points out that as he defines them, the two parameters are slightly mutated versions of the shape and scale as we’ve described them so far. The real way to use his parameters is to mutate them by doing this:
Summary!
So to summarize this adventure time blast, I have one key lesson: figure out which parameterization your software is using and which parameterization a model designer is using and make sure they match. Otherwise you’ll spend a terrible amount of time wondering why you just can’t estimate those variances accurately. I failed to do this a few weeks ago and was quite befuddled for a while, so don’t be like me.