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

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.

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, 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, and for prediction with respect to log-loss and 0-1 loss. All the functions here (except generate_data) are implementations of deterministic algorithms that have linear complexity in the length of the input data. Example data sets from finance, genetics and animal communication 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.' <arXiv:2007.14900> [stat.ME], July 2020].

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