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Monthly Archives: November 2016
DSL for Generative Models – Next Steps (61/365)
I’m going to sketch out the next few things to plan and code for the DSL library. I have so far provided the ability to describe the network but I haven’t yet provided a way to describe the distributions. For … Continue reading
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Lexicographical Ordering (60/365)
> import Data.Ord (comparing) This is completely random but I thought it was a neat use of laziness. You are familiar with lexicographical ordering? Haskell’s compare of lists implements this. ghci> [1,2] < [1,3] True ghci> [1,3,4] < [1,3,4,5] True … Continue reading
Learning a Language (59/365)
I want to start another aside because this is something I spend a significant amount of time thinking about. That is, how do you learn a new language when you are approaching 30? Living in India, I think it’s a … Continue reading
DSL for Generative Models (58/365)
I’ve now cleaned up (in befb0f3cca0c212e368497e86f030aa96355be18) the Reader and Writer interfaces and added it to Statistics.GModeling.Gibbs. I’ve removed references to Support and simply parameterized using a key type k and value type v. > data Reader k v = Reader … Continue reading
DSL for Generative Models (57/365)
I am putting together what I have so far in a repository here. So far, (133e22dc979d988706aafe52a346cee004f70ca5) it contains Statistics.GModeling.DSL Statistics.GModeling.Models.HMM Statistics.GModeling.Models.LDA Statistics.GModeling.Models.FixedTreeLDA Will continue building the pieces in upcoming posts.
Sharing a Birthday (56/365)
I think most have heard something like you only need suprisingly few people in a room before two people in the room end up sharing a birthday. But I never bothered to work it out. Let me do that. First, … Continue reading
Response Variable (55/365)
A quick aside. I was thinking about how response variables are attached to generative models. For instance, if we want to say have binary classification on documents we would normally 1) take the dot product the topic vector with a … Continue reading
DSL for Generative Models – Interpreter (54/365)
The next step is for the library to have access to the latent variable data. I also don’t want the library to decide how to store the data because the user will have a much better idea of what is … Continue reading
DSL for Generative Models – Interpreter (53/365)
In this post, I write some functions to interpret the DSL. Specifically, I present some functions to figure out the children and parents of a node and discover what the prior, observed, and latent variables are. > import Control.Monad (msum) … Continue reading
DSL for Generative Models – Examples (52/365)
In the previous post I attempted to introduce a DSL for probabilistic models inspired by the plate notation. Let’s try to see if we can define LDA with it. > data LDALabels = Alpha  Beta  Topics  Topic … Continue reading