SeqROCTM: A Matlab toolbox for the analysis of Sequence of Random Objects driven by Context Tree Models

TL;DR

The paper introduces SeqROCTM, a MATLAB toolbox for modeling probabilistic sequences of responses driven by context tree models, useful in auditory statistical learning research.

Contribution

It provides the first implementation of a new class of stochastic processes called sequences of random objects driven by context tree models, along with model selection procedures.

Findings

Provides a MATLAB toolbox for modeling sequences of random objects.

Includes algorithms for model selection in context tree models.

Facilitates research in auditory statistical learning.

Abstract

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.