Train a sequence predictor on a list of strings:

Predict the next character following a given string:

Predict the next four characters:

Obtain the probabilities for each character to follow the given string:

Train a sequence predictor on the list of common English words, each word treated as a sequence of characters:

Predict the most likely next character from a given sequence:

In the previous example, each word is considered as a subsequence of an infinite sequence. Use the character | to mark boundaries between words:

Build a new sequence predictor aware of word boundaries:

Generate the beginning of an English-like word:

Load a book from ExampleData:

Train a sequence predictor on this book:

Sample a random string in the book style:

Train another sequence predictor, interpreting strings as word sequences rather than character sequences:

Complete the preceding string with 10 consecutive words (spaces and punctuation marks are considered as words):

Download the "English" built-in sequence predictor:

Obtain the log-probability of the given string:

Preprocess the training text to predict on words rather than at the character level:

Complete the preceding string with 10 consecutive words (spaces and punctuation marks are considered as words):

Preprocess the training text to lowercase to obtain a better statistic with higher letter counts:

Train a classifier with an emphasis on the resulting model memory footprint:

Compare with the automatically generated model size:

Tune the computation time and precision when exploring the full sequence probability space:

Favor fast and approximated exploration:

Favor more in-depth exploration taking longer computation time:

Compare the results:

Specify a memory size of 3 for the Markov process trained on the training subsequences: