the sequence to be analysed. 
The sequence needs to be a "character" object. See the examples section of the BCT/kBCT functions on how to transform any dataset to a "character" object.

input_data

depth

set containing the symbols of the process. If not initialised, the default set contains all the unique symbols which appear in the sequence. 
This parameter is needed for the segmentation problem where short segments might not contain all the symbols in the alphabet.

desired_alphabet

hyper-parameter of the model prior. 
Takes values between 0 and 1. If not initialised in the call function, the default value is , 
where is the size of the alphabet; for more information see <a href="https://arxiv.org/pdf/2007.14900.pdf">Kontoyiannis et al. (2020)</a>.

beta

Computes the prior predictive likelihood of the data given a specific alphabet. This function is used in for change-point point/segmentation problems

An implementation of a collection of tools for exact Bayesian inference with discrete times series. This package contains functions that can be used for prediction, model selection, estimation, segmentation/change-point detection and other statistical tasks. Specifically, the functions provided can be used for the exact computation of the prior predictive likelihood of the data, for the identification of the a posteriori most likely (MAP) variable-memory Markov models, for calculating the exact posterior probabilities and the AIC and BIC scores of these models, for prediction with respect to log-loss and 0-1 loss and segmentation/change-point detection. Example data sets from finance, genetics, animal communication and meteorology are also provided. Detailed descriptions of the underlying theory and algorithms can be found in [Kontoyiannis et al. 'Bayesian Context Trees: Modelling and exact inference for discrete time series.' Journal of the Royal Statistical Society: Series B (Statistical Methodology), April 2022. Available at: <arXiv:2007.14900> [stat.ME], July 2020] and [Lungu et al. 'Change-point Detection and Segmentation of Discrete Data using Bayesian Context Trees' <arXiv:2203.04341> [stat.ME], March 2022].

Valentinian Lungu

Bayesian Context Trees for Discrete Time Series

CTW function

hyper-parameter of the model prior. 
Takes values between 0 and 1. If not initialised in the call function, the default value is , 
where is the size of the alphabet; for more information see <a href='https://arxiv.org/pdf/2007.14900.pdf'>Kontoyiannis et al. (2020)</a>.

CTW: Context Tree Weighting (CTW) algorithm

Description

Usage

Arguments

Value

See Also

Examples