Markov Chain Generator
Paste sample text and generate new text in the same style using Markov chains. Adjust the n-gram order to see how statistical models learn language patterns.
Considers the previous 2 words when choosing the next word. Good balance of creativity and coherence.
Generated Text
Click any word to see its transition probabilitiesTransition Probabilities
Markov chains are statistical models that predict the next item in a sequence based only on the current state. For text, this means choosing the next word based on the preceding words. They're the foundation of many language models and NLP techniques.
N-gram order determines how many preceding words the model considers. Order 1 (unigram) picks words randomly weighted by frequency. Order 2 (bigram) looks at the previous word. Order 3 (trigram) looks at the previous two words, and so on. Higher orders produce more coherent but less creative text.
Transition probabilities show which words can follow a given context and how likely each one is. Click any word in the generated text to see its probability distribution — this is exactly how the algorithm "thinks."
Try this: Generate text at order 1 and then at order 5. Notice how the output evolves from random word soup to eerily convincing prose. This progression illustrates a fundamental concept in computational linguistics.