Generating Learning Algorithms: Hidden Markov Models as a Case Study
Daniel Szymczak · MacSphere (McMaster University) · 2014
This thesis presents the design and implementation of a source code generator for dealing with Bayesian statistics. The specific focus of this case study is to produce usable source code for handling Hidden Markov Models (HMMs) from a Domain Specific Language (DSL). Domain specific languages are used to allow domain experts to design their source code from the perspective of the problem domain. The goal of designing in such a way is to increase the development productivity without requiring extensive programming knowledge.