# k-Bayesian Context Trees (kBCT) algorithm Finds the top k a posteriori most likely tree models. ```r kBCT(input_data, depth, k, beta = NULL) ``` ## Arguments - `input_data`: the sequence to be analysed. The sequence needs to be a "character" object. See the examples section on how to transform any dataset to a "character" object. - `depth`: maximum memory length. - `k`: number of the a posteriori most likely tree models to be identified. - `beta`: 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: [Kontoyiannis et al. (2020)](https://arxiv.org/pdf/2007.14900.pdf). ## Returns a list object which includes: - **Contexts**: top k a posteriori most likely models. Each model given as a list object containing the contexts of its leaves. - **Results**: a dataframe with the following columns: prior probability, log(prior probability), posterior probability, log(posterior probability), posterior odds, number of leaves, maximum depth, BIC score, AIC score and maximum log-likelihood. ## Examples ```r # Finding the first 5 a posteriori most likely models with maximum depth <= 5 # for the SP500 dataset (with default value beta): kBCT(SP500, 5, 2) # For custom beta (e.g. 0.8): kBCT(SP500, 5, 2, 0.8) # The type of the input dataset is "character" # If the dataset is contained within a vector: q <- c(1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0) # Convert a vector to a "character" object: s <- paste(q, collapse = "") kBCT(s, 2, 2) # Reading a file using the readChar function # Example 1: The dataset is stored in a .txt file # fileName <- '~/example_data.txt' # fileName stores the path to the dataset # s<-readChar(fileName, file.info(fileName)$size) # Make sure that s does not contain any "\n" at the end of the string # To remove last entry: # s<-gsub('.$', '', s) # To remove any unwanted characters (e.g. "\n"): # s<-gsub('\n', '', s) # Example 2: The dataset is stored in a .csv file # fileName <- '~/example_data.csv' # fileName stores the path to the dataset # s<-readChar(fileName, file.info(fileName)$size) # Depending on the running environment, # s might contain unwanted characters such as: "\n" or "\r\n". # Remove any unwanted characters (e.g. "\r\n"): # s<-gsub('\r\n', '', s) # Always make sure that s does not contain any unwanted characters ``` ## See Also `BCT`

kBCT function