SeqROCTM: A Matlab toolbox for the analysis of Sequence of Random Objects driven by Context Tree Models
This provides a tool for researchers in auditory statistical learning and related fields to analyze complex sequence data, though it is incremental as it builds on existing context tree models.
The authors tackled the problem of modeling the relationship between probabilistic sequences of inputs and responses by introducing SeqROCTM, a Matlab toolbox that implements a new class of stochastic processes called sequences of random objects driven by context tree models, along with three model selection procedures for inference.
In several research problems we deal with probabilistic sequences of inputs (e.g., sequence of stimuli) from which an agent generates a corresponding sequence of responses and it is of interest to model the relation between them. A new class of stochastic processes, namely \textit{sequences of random objects driven by context tree models}, has been introduced to model such relation in the context of auditory statistical learning. This paper introduces a freely available Matlab toolbox (SeqROCTM) that implements this new class of stochastic processes and three model selection procedures to make inference on it. Besides, due to the close relation of the new mathematical framework with context tree models, the toolbox also implements several existing model selection algorithms for context tree models.