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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
DSL for Generative Models (51/365)
The backlog becomes longer. I’ve changed jobs two weeks ago and it has upset my routine. No matter. Here we go again. I want to deviate from my problem solving mode for a while and use up a few posts … Continue reading
Random Variables – Problem (50/365)
Moving on to the next chapter “Random Variables – I”, take a look at the following problem. Show that the random variable is continuous if and only if for all . (Forward direction) Suppose is a continuous random variable, then … Continue reading
Measurable Spaces – Problem (49/365)
Let be the LebesqueStieltjes measure generated by a continuous function. Show that if the set is at most countable, then . A LebesqueStieltjes measure is a countably additive measure and is given by a generalized distribution function such that that … Continue reading
Measurable Spaces – Problem (48/365)
Another question on distribution functions. Show that each of the functions is continuous on the right but is not a distribution function in . Take the first function. To show that it is continuous on the right, let and let … Continue reading