Uncategorized

Generating Text Using a Markov Model

alexhwoods

Screen Shot 2015-08-05 at 10.57.18 AM

A Markov Chain is a random process, where we assume the previous state(s) hold sufficient predictive power in predicting the next state. Unlike flipping a coin, these events are dependent. It’s easier to understand through an example.

Imagine the weather can only be rainy or sunny. That is, the state space is rainy or sunny. We can represent our Markov model as a transition matrix, with each row being a state, and each column being the probability it moves to another.

f5f65f94c2f9cd72f9f2ce10499e6b7b However, it’s easier to understand with this state transition diagram.

220px-Markov_Chain_weather_model_matrix_as_a_graph

In other words, given today is sunny, there is a .9 probability that tomorrow will be sunny, and a .1 probability that tomorrow will be rainy.

Text Generator

One cool application of this is a language model, in which we predict the next word based on the current word(s). If we just predict based on the last word…

View original post 507 more words

Advertisements
Standard

One thought on “Generating Text Using a Markov Model

  1. Pingback: Markov model meets Stanislaw Lem | Eyes All Around

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s